{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\OUTPUT\Performance Management.APPENDIX F MODELS.F4_F6.04-10-2025.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}10 Apr 2025, 18:36:19
{txt}
{com}. 
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. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
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. 
. *** APPENDIX F MODELS: PLACEBO INTEVRENTION MODELS [INCLUDE: ORIGINAL 'ADOPTION' TREATMENTS AS CONTROL COVARIATES ESTIMATED LINBEARILY VIA PARAMETRIC METHODS FOR COMPUTATIONAL NECESSITY PURPOSES TO AVOID 'CURSE OF DIMENSIONALITY'] -- MODELS F4--F6 
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. *** 'PLACEBO TREATMENT' IS THE IT REFORM PROGRAM/PROJECT START DATE THROUGH ITS DEVELOPMENT -- ALL 'ADOPTION/TREATMENT' OBSERVATIONS ARE OMITTED FROM THIS EFFECTIVE SAMPLE OF OBSERVATIONS [FIGURES F3-F4] ****
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. ********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
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. 
. *** MODELS PREDICTING VARIOPUS TYPE OF PROGRAM ERROR RATES BASED ON BAM SAMPLING RATES ***
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. 
. 
. *** MODEL F4: OVERALL ERROR RATE: (SAMPLE WEIGHTED) ***
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. *  [# overpayment errors / paid claims sample] + [# underpayment errors / paid claims sample] + [# erroneous denials / denied claims sample] + [# underpayment errors / denied claims sample] *
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. 
. 
. *** MODEL F5: ABSOLUTE TYPE I ERROR RATE ***
. 
. * [overpayment error rate / paid claims sample] *
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. 
. 
. 
. *** MODEL F6: RELATIVE TYPE I ERROR RATE:  {c -(}TYPE I ERROR RATE /  [TYPE I ERROR RATE + TYPE II ERROR RATE]{c )-}      (SAMPLE WEIGHTED)  ***
. 
. *  {c -(}[overpayment error rate / paid claims sample]   /  [overpayment error rate / paid claims sample]   +  [underpayment error rate / paid claims sample]  +  [erroneous denial / denied claims sample]  +  [underpayment error / denied claims sample]{c )-}  *
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. ********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
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. 
. *** RETRIEVE MANUSCRIPT DATABASE [as of 04-10-2025] ***
. 
. use "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\DATA\Performance Management.MANUSCRIPT DATABASE.04-10-2025.dta", replace
{txt}
{com}. 
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. 
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. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
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. 
. 
. *** SET DATA TO PANEL STRUCTURE  ***
. 
. xtset stateid monthyear, monthly
{res}
{col 1}{txt:Panel variable: }{res:stateid}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:monthyear}{txt:, }{res:{bind:2002m1}}{txt: to }{res:{bind:2022m9}}{p_end}
{txt}{col 10}Delta: {res}1 month
{txt}
{com}. 
. *
. *
. *
. *
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. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
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. 
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. ** NOTE: WILL NEED TO EXCLUDE THE FOLLOWING STATE PANELS SINCE THE START DATES OCCUR PRIOR TO THE SAMPLE PERIOD THAT COMMENCES ON 1/2002 **
. 
. 
. * NEW MEXICO (1ST IT REFORM ADOPTION/INSTITUTION: NOVEMBER 2002): STATEID==31; PROJECT START DATE: MAY 2001 *
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. * OHIO (IT REFORM ADOPTION/INSTITUTION: AUGUST 2004): STATEID==35; PROJECT START DATE: JANUARY 2000 *
.  
. * UTAH (IT REFORM ADOPTION/INSTITUTION: JANUARY 2006): STATEID==44; PROJECT START DATE: OCTOBER 2000 *
. 
. 
. drop if stateid==31 | stateid==35 | stateid==44
{txt}(738 observations deleted)

{com}. 
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. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
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. 
. 
. *** CREATE 'PLACEBO' INTERVENTION/TREATMENT VARIABLES BASED ON PROJECT START DATE *** 
. 
. ** create project start date counter that equals "1" for first month of project start date and increase by increments of one per each successive month until the IT reform is adopted/instituted/launched at monthyear "t + s"
. 
. gen itmod_monthcount_placebo = itmod_projectstartmonth
{txt}(10,206 missing values generated)

{com}. * 
. *
. * replace missing values for itmod_monthcount_placebo based on itmod_projectstartmonth missing values only for the pre-start date 'placebo' intervention/treatment periods [while leave adoption/institution of IT reform periods missing values as appears with itmod_projectstartmonth variable since these are to be excluded from the effective sample for this analysis 
. 
. * NOTE: FOR THE ALTERNATIVE F4-F6 MODELS [I.E., FIGURES F3 & F4] INCORPORATING THE 'ADOPTION' TREATMENT AS A LINEAR, ADDITIVE CONTROL COVARIATE REQUIRES THE 2ND "REPLACE" COMMAND BELOW THAT INCREASES THE 'PLACEBO' TREATMENT BY INCREMENTS OF + 1 AT TIME OF ADOPTION/POST-ADOPTION ONCE IT REFORM IS LAUNCHED TO ENSURE POST-ADOPTION OBSERVATIONS DO NOT DROP OUT FROM STATISTICAL ESTIMATION *    
. 
. replace itmod_monthcount_placebo = 0 if itmod_monthcount_placebo==. & itmod_monthcount==0
{txt}(7,888 real changes made)

{com}. *
. replace itmod_monthcount_placebo = itmod_monthcount_placebo[_n-1] + 1  if itmod_monthcount_placebo==. & itmod_monthcount>0
{txt}(2,318 real changes made)

{com}. 
. 
. 
. 
. ** create binary 'placebo' treatment indicator [= 0 prior to start date; = 1 during project development phase (start date --> month prior to adoptiojn/launch of IT reforms)] 
. 
. gen itmod_monthcount_placebo_binary = 1 if itmod_monthcount_placebo>0
{txt}(8,119 missing values generated)

{com}. * 
. replace itmod_monthcount_placebo_binary = 0 if itmod_monthcount_placebo==0
{txt}(8,119 real changes made)

{com}. 
. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** GENERATE PROJECT START YEAR COHORT UNIT/FE BINARY INDICATORS ***
. *** [TO COMPLEMENT STANDARD STATE & YEAR BINARY INDICATORS -- THE SYBIs ACCOUNT FOR SEQUENCE OF STAGGERED PROJECT START DATES ON PERFORMANCE 
. *** RESULTING FROM IT MODERNIZATION REFORM 'PLACEBO TREATMENTS' TAKING PLACE] ***
. 
. 
. 
. *** PURPOSE: CONTROL FOR STAGGERED/HETEROGENOUS SEQUENCE OF IT MODERNIZATION BEING STARTED AT VARIOUS TIMES IN DIFFERENT YEARS AS PROJECT START YEAR-TIME UNIT EFFECTS ***
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. 
. 
. 
. *** 2003 IT PROJECT START DATES [MINNESOTA] ***
. generate startcohort_2003=1 if stateid==23
{txt}(11,567 missing values generated)

{com}. *
. replace startcohort_2003=0 if startcohort_2003==.
{txt}(11,567 real changes made)

{com}. 
. 
. 
. *** 2004 PROJECT START DATES [MISSISSIPPI, NEBRASKA (1ST IT REFORM)] ***
. generate startcohort_2004=1 if stateid==24 | stateid==27
{txt}(11,321 missing values generated)

{com}. * 
. replace startcohort_2004=0 if startcohort_2004==.
{txt}(11,321 real changes made)

{com}. 
. 
. 
. *** 2005 PROJECT START DATES [ILLINOIS, INDIANA] ***
. generate startcohort_2005=1 if stateid==13 | stateid==14
{txt}(11,321 missing values generated)

{com}. * 
. replace startcohort_2005=0 if startcohort_2005==.
{txt}(11,321 real changes made)

{com}. 
. 
. 
. *** 2006 PROJECT START DATES [NEW HAMPSHIRE] ***
. generate startcohort_2006=1 if stateid==29
{txt}(11,567 missing values generated)

{com}. * 
. replace startcohort_2006=0 if startcohort_2006==.
{txt}(11,567 real changes made)

{com}. 
. 
. 
. *** 2007 PROJECT START DATES [MASSACHUSETTS, NEW MEXICO (2ND IT REFORM] ***
. generate startcohort_2007=1 if stateid==21 | stateid==52 
{txt}(11,321 missing values generated)

{com}. * 
. replace startcohort_2007=0 if startcohort_2007==.
{txt}(11,321 real changes made)

{com}. 
. 
. *** 2008 PROJECT START DATES [FLORIDA] ***
. generate startcohort_2008=1 if stateid==9 
{txt}(11,567 missing values generated)

{com}. * 
. replace startcohort_2008=0 if startcohort_2008==.
{txt}(11,567 real changes made)

{com}. 
. 
. 
. *** 2009 IT PROJECT START DATES [CALIFORNIA; MISSISSIPPI, NEW HAMPSHIRE, & VIRGINIA] ***
. generate startcohort_2009=1 if stateid==5 | stateid==24 |  stateid==29 | stateid==46
{txt}(10,829 missing values generated)

{com}. * 
. replace startcohort_2009=0 if startcohort_2009==.
{txt}(10,829 real changes made)

{com}. 
. 
. 
. *** 2010 PROJECT START DATES [ILLINOIS, NEVADA] ***
. generate startcohort_2010=1 if stateid==13 | stateid==28
{txt}(11,321 missing values generated)

{com}. * 
. replace startcohort_2010=0 if startcohort_2010==.
{txt}(11,321 real changes made)

{com}. 
. 
. 
. 
. *** 2012 PROJECT START DATES [IDAHO, LOUISIANA, MICHIGAN, MISSOURI] ***
. generate startcohort_2012=1 if stateid==12 | stateid==18 | stateid==22 | stateid==25
{txt}(10,826 missing values generated)

{com}. * 
. replace startcohort_2012=0 if startcohort_2012==.
{txt}(10,826 real changes made)

{com}. 
. 
. 
. *** 2013 PROJECT START DATES [MAINE, NEBRASKA (2ND IT REFORM), NORTH CAROLINA] ***
. generate startcohort_2013=1 if stateid==19 | stateid==51 | stateid==33 
{txt}(11,075 missing values generated)

{com}. * 
. replace startcohort_2013=0 if startcohort_2013==.
{txt}(11,075 real changes made)

{com}. 
. 
. 
. *** 2014 PROJECT START DATES [TENNESSEE] ***
. generate startcohort_2014=1 if stateid==42 
{txt}(11,567 missing values generated)

{com}. * 
. replace startcohort_2014=0 if startcohort_2014==.
{txt}(11,567 real changes made)

{com}. 
. 
. 
. *** 2015 PROJECT START DATES [MARYLAND, WASHINGTON] ***
. generate startcohort_2015=1 if stateid==20 | stateid==47    
{txt}(11,321 missing values generated)

{com}. * 
. replace startcohort_2015=0 if startcohort_2015==.
{txt}(11,321 real changes made)

{com}. 
. 
. 
. *** 2016 PROJECT START DATES [SOUTH CAROLINA] ***
. generate startcohort_2016=1 if stateid==40
{txt}(11,567 missing values generated)

{com}. *
. replace startcohort_2016=0 if startcohort_2016==.
{txt}(11,567 real changes made)

{com}. *
. 
. 
. *** 2017 PROJECT START DATES [ALABAMA, COLORADO, PENNSYLVANIA, WYOMING] ***
. generate startcohort_2017=1 if stateid==1 | stateid==6 | stateid==38 | stateid==50    
{txt}(10,826 missing values generated)

{com}. * 
. replace startcohort_2017=0 if startcohort_2017==.
{txt}(10,826 real changes made)

{com}. 
. 
. 
. **********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. *** COMPUTE PROJECT START DATE YEAR COHORT * 'PLACEBO' TREATMENT INTERACTION EFFECTS ***
. *** [state group project start date cohort [based on common year of project start date] * 'placebo' treatment effect ***                                                                                                    
. 
. *** NOTE: This is activated as "1" starting in the exact month when the IT reform project start date commences within a given year to ensure pre-'placebo' treatment monthly observations wihtin a adoption year take on "0" values] ***
. 
. *
. *
. *
. 
. generate startcohort_2003_itstart = startcohort_2003*itmod_monthcount_placebo_binary
{txt}
{com}. *
. generate startcohort_2004_itstart = startcohort_2004*itmod_monthcount_placebo_binary
{txt}
{com}. *
. generate startcohort_2005_itstart = startcohort_2005*itmod_monthcount_placebo_binary
{txt}
{com}. *
. generate startcohort_2006_itstart = startcohort_2006*itmod_monthcount_placebo_binary
{txt}
{com}. *
. generate startcohort_2007_itstart = startcohort_2007*itmod_monthcount_placebo_binary
{txt}
{com}. *
. generate startcohort_2008_itstart = startcohort_2008*itmod_monthcount_placebo_binary
{txt}
{com}. *
. generate startcohort_2009_itstart = startcohort_2009*itmod_monthcount_placebo_binary
{txt}
{com}. *
. generate startcohort_2010_itstart = startcohort_2010*itmod_monthcount_placebo_binary
{txt}
{com}. *
. *
. *
. generate startcohort_2012_itstart = startcohort_2012*itmod_monthcount_placebo_binary
{txt}
{com}. *
. generate startcohort_2013_itstart = startcohort_2013*itmod_monthcount_placebo_binary
{txt}
{com}. *
. generate startcohort_2014_itstart = startcohort_2014*itmod_monthcount_placebo_binary
{txt}
{com}. *
. generate startcohort_2015_itstart = startcohort_2015*itmod_monthcount_placebo_binary
{txt}
{com}. *
. generate startcohort_2016_itstart = startcohort_2016*itmod_monthcount_placebo_binary
{txt}
{com}. *
. generate startcohort_2017_itstart = startcohort_2017*itmod_monthcount_placebo_binary
{txt}
{com}. *
. *
. *
. *     
. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. *** COMPUTE CATEGORICAL TASK COMPLEXITY COVARIATE MEASURES [CONDITIONAL ADAPTATION TO IT MODERNIZATION REFORMS] ***
. 
. ** PURPOSE: COMPUTE MARGINAL DIFFERENTIAL EFFECTS IN MANUSCRIPT MODELS [BASED ON EFFECTIVE SAMPLE OF OBSERVATIONS] **
. 
. 
. 
. ** (1) INTERSTATE CASE RATES [PAID & DENIED CLAIMS SAMPLES: TOTAL ERROR RATE & RELATIVE TYPE I ERROR RATE [MODELS F4 & F6];  PAID CLAIMS SAMPLE ONLY: ABSOLUTE TYPE I ERROR RATE [MODEL F5] **
. 
. 
. * Overall Program Error Rate *
. 
. quietly reg totalerror_rat  itmod_monthcount_placebo  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  itmod_monthcount  startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart  if itmod_adopt_state==1 
{txt}
{com}. *
. *
. sum tot_interstate if e(sample), detail

                       {txt}tot_interstate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res} .0222222              0       {txt}Obs         {res}      6,293
{txt}25%    {res} .0444444              0       {txt}Sum of wgt. {res}      6,293

{txt}50%    {res} .0833333                      {txt}Mean          {res} .1115305
                        {txt}Largest       Std. dev.     {res} .1054718
{txt}75%    {res} .1449631       .7738096
{txt}90%    {res} .2261905       .7785548       {txt}Variance      {res} .0111243
{txt}95%    {res} .3063973        .781746       {txt}Skewness      {res} 2.305618
{txt}99%    {res} .5555556       .7857143       {txt}Kurtosis      {res} 10.48716
{txt}
{com}. di r(p75)
{res}.14496315
{txt}
{com}. di r(p25)
{res}.04444445
{txt}
{com}. *
. gen tot_interstate_catF2 =.
{txt}(11,813 missing values generated)

{com}. replace tot_interstate_catF2 = 0 if tot_interstate<= r(p25) 
{txt}(2,348 real changes made)

{com}. replace tot_interstate_catF2 = 1 if tot_interstate> r(p25) & tot_interstate < r(p75) 
{txt}(5,237 real changes made)

{com}. replace tot_interstate_catF2 = 2 if tot_interstate>= r(p75) 
{txt}(4,228 real changes made)

{com}. *
. tab tot_interstate_catF2 if e(sample)

{txt}tot_interst {c |}
  ate_catF2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,625       25.82       25.82
{txt}          1 {c |}{res}      3,093       49.15       74.97
{txt}          2 {c |}{res}      1,575       25.03      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,293      100.00
{txt}
{com}. 
. 
. *
. *
. *
. 
. * Absolute Type I Error Rate *
. 
. quietly reg t1error_rat  itmod_monthcount_placebo  t1_interstate    t1_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat  i.stateid i.year  itmod_monthcount  startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart  if itmod_adopt_state==1 
{txt}
{com}. *
. *
. sum t1_interstate if e(sample), detail

                        {txt}t1_interstate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      6,533
{txt}25%    {res}      .02              0       {txt}Sum of wgt. {res}      6,533

{txt}50%    {res}      .04                      {txt}Mean          {res} .0549132
                        {txt}Largest       Std. dev.     {res} .0587484
{txt}75%    {res}     .075       .4117647
{txt}90%    {res}     .125       .4222222       {txt}Variance      {res} .0034514
{txt}95%    {res} .1666667       .4285714       {txt}Skewness      {res} 2.074142
{txt}99%    {res} .2857143       .4285714       {txt}Kurtosis      {res} 9.187309
{txt}
{com}. di r(p75)
{res}.075
{txt}
{com}. di r(p25)
{res}.02
{txt}
{com}. *
. gen t1_interstate_catF2 =.
{txt}(11,813 missing values generated)

{com}. replace t1_interstate_catF2 = 0 if t1_interstate<= r(p25) 
{txt}(2,494 real changes made)

{com}. replace t1_interstate_catF2 = 1 if t1_interstate> r(p25) & t1_interstate < r(p75) 
{txt}(5,295 real changes made)

{com}. replace t1_interstate_catF2 = 2 if t1_interstate>= r(p75) 
{txt}(4,024 real changes made)

{com}. *
. tab t1_interstate_catF2 if e(sample)

{txt}t1_intersta {c |}
   te_catF2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,654       25.32       25.32
{txt}          1 {c |}{res}      3,143       48.11       73.43
{txt}          2 {c |}{res}      1,736       26.57      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,533      100.00
{txt}
{com}. *
. *
. *
. 
. * Relative Type I Error Rate [same as Overall Program Error Rate since Contains Both Type I & Type II Program Error Rates] *
. 
. quietly reg relt1error_rat  itmod_monthcount_placebo  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  itmod_monthcount  startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart   if itmod_adopt_state==1
{txt}
{com}. *
. *
. sum tot_interstate if e(sample), detail

                       {txt}tot_interstate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res} .0222222              0       {txt}Obs         {res}      6,086
{txt}25%    {res} .0444444              0       {txt}Sum of wgt. {res}      6,086

{txt}50%    {res} .0833333                      {txt}Mean          {res} .1110138
                        {txt}Largest       Std. dev.     {res}  .104107
{txt}75%    {res} .1452174       .7738096
{txt}90%    {res} .2246022       .7785548       {txt}Variance      {res} .0108383
{txt}95%    {res} .3026961        .781746       {txt}Skewness      {res} 2.299545
{txt}99%    {res} .5555556       .7857143       {txt}Kurtosis      {res} 10.53351
{txt}
{com}. di r(p75)
{res}.14521739
{txt}
{com}. di r(p25)
{res}.04444445
{txt}
{com}. *
. gen relt1_interstate_catF2 =.
{txt}(11,813 missing values generated)

{com}. replace relt1_interstate_catF2 = 0 if tot_interstate<= r(p25) 
{txt}(2,348 real changes made)

{com}. replace relt1_interstate_catF2 = 1 if tot_interstate> r(p25) & tot_interstate < r(p75) 
{txt}(5,241 real changes made)

{com}. replace relt1_interstate_catF2 = 2 if tot_interstate>= r(p75) 
{txt}(4,224 real changes made)

{com}. *
. tab relt1_interstate_catF2 if e(sample)

{txt}relt1_inter {c |}
state_catF2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,568       25.76       25.76
{txt}          1 {c |}{res}      2,996       49.23       74.99
{txt}          2 {c |}{res}      1,522       25.01      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,086      100.00
{txt}
{com}. *
. 
. 
. 
. *****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. ** (2) DIFFERENT OCCUPATION SEEKING RATE [PAID & DENIED CLAIMS SAMPLES: TOTAL ERROR RATE & RELATIVE TYPE I ERROR RATE [MODELS F4 & F6];  PAID CLAIMS SAMPLE ONLY: ABSOLUTE TYPE I ERROR RATE [MODEL F5] **
. 
. 
. * Overall Program Error Rate *
. 
. quietly reg totalerror_rat  itmod_monthcount_placebo  tot_interstate   tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  itmod_monthcount startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart  if itmod_adopt_state==1 
{txt}
{com}. *
. *
. sum tot_diffoccupseek if e(sample), detail

                      {txt}tot_diffoccupseek
{hline 61}
      Percentiles      Smallest
 1%    {res} .1276596              0
{txt} 5%    {res} .2121212              0
{txt}10%    {res} .2725225              0       {txt}Obs         {res}      6,293
{txt}25%    {res} .3888889              0       {txt}Sum of wgt. {res}      6,293

{txt}50%    {res} .5333334                      {txt}Mean          {res} .5397037
                        {txt}Largest       Std. dev.     {res} .2080693
{txt}75%    {res} .6804054       1.181818
{txt}90%    {res} .8166667       1.207547       {txt}Variance      {res} .0432929
{txt}95%    {res}       .9       1.219512       {txt}Skewness      {res} .2408608
{txt}99%    {res} 1.053571       1.299107       {txt}Kurtosis      {res} 2.756693
{txt}
{com}. di r(p75)
{res}.68040538
{txt}
{com}. di r(p25)
{res}.3888889
{txt}
{com}. *
. gen tot_diffoccupseek_catF2 =.
{txt}(11,813 missing values generated)

{com}. replace tot_diffoccupseek_catF2 = 0 if tot_diffoccupseek<= r(p25) 
{txt}(2,921 real changes made)

{com}. replace tot_diffoccupseek_catF2 = 1 if tot_diffoccupseek> r(p25) & tot_diffoccupseek < r(p75) 
{txt}(5,615 real changes made)

{com}. replace tot_diffoccupseek_catF2 = 2 if tot_diffoccupseek>= r(p75) 
{txt}(3,277 real changes made)

{com}. *
. tab tot_diffoccupseek_catF2 if e(sample)

{txt}tot_diffocc {c |}
upseek_catF {c |}
          2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,585       25.19       25.19
{txt}          1 {c |}{res}      3,134       49.80       74.99
{txt}          2 {c |}{res}      1,574       25.01      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,293      100.00
{txt}
{com}. 
. *
. *
. *
. 
. * Absolute Type I Error Rate *
. 
. quietly reg t1error_rat  itmod_monthcount_placebo  t1_interstate    t1_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat  i.stateid i.year  itmod_monthcount  startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart   if itmod_adopt_state==1 
{txt}
{com}. *
. *
. sum t1_diffoccupseek if e(sample), detail

                      {txt}t1_diffoccupseek
{hline 61}
      Percentiles      Smallest
 1%    {res} .0425532              0
{txt} 5%    {res} .0930233              0
{txt}10%    {res}     .125              0       {txt}Obs         {res}      6,533
{txt}25%    {res} .1842105              0       {txt}Sum of wgt. {res}      6,533

{txt}50%    {res}      .25                      {txt}Mean          {res} .2575703
                        {txt}Largest       Std. dev.     {res} .1068131
{txt}75%    {res} .3243243       .7142857
{txt}90%    {res}       .4       .7142857       {txt}Variance      {res}  .011409
{txt}95%    {res} .4444444       .8214286       {txt}Skewness      {res}  .408537
{txt}99%    {res} .5357143       .8214286       {txt}Kurtosis      {res} 3.348662
{txt}
{com}. di r(p75)
{res}.32432431
{txt}
{com}. di r(p25)
{res}.18421052
{txt}
{com}. *
. gen t1_diffoccupseek_catF2 =.
{txt}(11,813 missing values generated)

{com}. replace t1_diffoccupseek_catF2 = 0 if t1_diffoccupseek<= r(p25) 
{txt}(3,375 real changes made)

{com}. replace t1_diffoccupseek_catF2 = 1 if t1_diffoccupseek> r(p25) & t1_diffoccupseek < r(p75) 
{txt}(5,563 real changes made)

{com}. replace t1_diffoccupseek_catF2 = 2 if t1_diffoccupseek>= r(p75) 
{txt}(2,875 real changes made)

{com}. *
. tab t1_diffoccupseek_catF2

{txt}t1_diffoccu {c |}
pseek_catF2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      3,375       28.57       28.57
{txt}          1 {c |}{res}      5,563       47.09       75.66
{txt}          2 {c |}{res}      2,875       24.34      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     11,813      100.00
{txt}
{com}. tab t1_diffoccupseek_catF2 if itmod_adopt_state==1

{txt}t1_diffoccu {c |}
pseek_catF2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,664       25.03       25.03
{txt}          1 {c |}{res}      3,211       48.30       73.33
{txt}          2 {c |}{res}      1,773       26.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,648      100.00
{txt}
{com}. *
. *
. *
. 
. * Relative Type I Error Rate [same as Overall Program Error Rate since Contains Both Type I & Type II Program Error Rates] *
. 
. quietly reg relt1error_rat  itmod_monthcount_placebo  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  itmod_monthcount  startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart  if itmod_adopt_state==1 
{txt}
{com}. *
. *
. sum tot_diffoccupseek if e(sample), detail

                      {txt}tot_diffoccupseek
{hline 61}
      Percentiles      Smallest
 1%    {res} .1333333       .0181818
{txt} 5%    {res} .2146572       .0277778
{txt}10%    {res} .2745466       .0548048       {txt}Obs         {res}      6,086
{txt}25%    {res} .3888889       .0604082       {txt}Sum of wgt. {res}      6,086

{txt}50%    {res} .5333334                      {txt}Mean          {res} .5411086
                        {txt}Largest       Std. dev.     {res} .2075091
{txt}75%    {res} .6814087       1.181818
{txt}90%    {res} .8190477       1.207547       {txt}Variance      {res}   .04306
{txt}95%    {res} .9024024       1.219512       {txt}Skewness      {res}   .26532
{txt}99%    {res} 1.055556       1.299107       {txt}Kurtosis      {res} 2.726246
{txt}
{com}. di r(p75)
{res}.6814087
{txt}
{com}. di r(p25)
{res}.3888889
{txt}
{com}. *
. gen relt1_diffoccupseek_catF2 =.
{txt}(11,813 missing values generated)

{com}. replace relt1_diffoccupseek_catF2 = 0 if tot_diffoccupseek<= r(p25) 
{txt}(2,921 real changes made)

{com}. replace relt1_diffoccupseek_catF2 = 1 if tot_diffoccupseek> r(p25) & tot_diffoccupseek < r(p75) 
{txt}(5,631 real changes made)

{com}. replace relt1_diffoccupseek_catF2 = 2 if tot_diffoccupseek>= r(p75) 
{txt}(3,261 real changes made)

{com}. *
. tab relt1_diffoccupseek_catF2 if e(sample)

{txt}relt1_diffo {c |}
ccupseek_ca {c |}
        tF2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,530       25.14       25.14
{txt}          1 {c |}{res}      3,034       49.85       74.99
{txt}          2 {c |}{res}      1,522       25.01      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      6,086      100.00
{txt}
{com}. 
. 
. 
. ***********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** MODELS F4-F6: "TASK COMPLEXITY, ORGANIZATIONAL ADAPTATION & PROGRAM ERROR RATES" 'PLACEBO TREATMENT INTERVENTION' STATISTICAL ANALYSES [JULY 2024]: ORGANIZATIONAL ADAPTATION EFFECTS ON PROGRAM PAYMENT ERROR RATES [FM4: TOTAL PROGRAM ERROR RATE; FM5: ABSOLUTE TYPE I PROGRAM ERROR RATE; FM6: RELATIVE TYPE I PROGRAM ERROR RATE] **** 
. 
. 
. ** ALTERNATIVE 'PLACEBO' TREATMENT EFFECT MODELS INCORPORATING 'ADOPTION' TEATMENT EFFECTS [UNLIKE ORIGINAL APPENDIX F ANALYSES.12-08-2024] ** 
. 
. 
. 
. ** (MODEL F4; FIGURES F3A-3C; MODEL F5: FIGURES F4A-F4C; MODEL F6: FIGURES F4D-F4F) **
. 
. 
. 
. 
. 
. ************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** TESTING H1 & H3: TOTAL/OVERALL PROGRAM ERROR RATE ORGANIZATIONAL ADAPTATION  ['PLACEBO' INTERVENTION/TREATMENT MODELS] ***
. 
. 
. 
. *** ESTIMATE MODEL F4: TOTAL PROGRAM ERROR  RATE [PROPORTION OF SAMPLE-WEIGHTED CASES OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [CONTROLS, PLUS STATE, YEAR, AND YEAR-ADOPTION COHORT UNIT EFFECTS] ***         (FIGURES F3A-F3C) 
. 
. npregress series totalerror_rat  itmod_monthcount_placebo  i.tot_interstate_catF2   i.tot_diffoccupseek_catF2  if itmod_adopt_state==1, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat    i.stateid i.year itmod_monthcount  startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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done
{res}
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{txt}{space 23}28  {c |}{col 28}{res}{space 2}   .07471{col 40}{space 2} .0099477{col 51}{space 1}    7.51{col 60}{space 3}0.000{col 68}{space 4} .0552129{col 81}{space 3} .0942071
{txt}{space 23}29  {c |}{col 28}{res}{space 2} .1700703{col 40}{space 2} .0220395{col 51}{space 1}    7.72{col 60}{space 3}0.000{col 68}{space 4} .1268738{col 81}{space 3} .2132669
{txt}{space 23}33  {c |}{col 28}{res}{space 2} .0075282{col 40}{space 2} .0135297{col 51}{space 1}    0.56{col 60}{space 3}0.578{col 68}{space 4}-.0189896{col 81}{space 3} .0340459
{txt}{space 23}38  {c |}{col 28}{res}{space 2} .1828345{col 40}{space 2} .0267169{col 51}{space 1}    6.84{col 60}{space 3}0.000{col 68}{space 4} .1304702{col 81}{space 3} .2351987
{txt}{space 23}40  {c |}{col 28}{res}{space 2} .0663161{col 40}{space 2}  .011448{col 51}{space 1}    5.79{col 60}{space 3}0.000{col 68}{space 4} .0438785{col 81}{space 3} .0887537
{txt}{space 23}42  {c |}{col 28}{res}{space 2} .1632844{col 40}{space 2} .0109871{col 51}{space 1}   14.86{col 60}{space 3}0.000{col 68}{space 4}   .14175{col 81}{space 3} .1848188
{txt}{space 23}46  {c |}{col 28}{res}{space 2}  .082889{col 40}{space 2} .0157615{col 51}{space 1}    5.26{col 60}{space 3}0.000{col 68}{space 4} .0519971{col 81}{space 3}  .113781
{txt}{space 23}47  {c |}{col 28}{res}{space 2}-.0134566{col 40}{space 2} .0184959{col 51}{space 1}   -0.73{col 60}{space 3}0.467{col 68}{space 4} -.049708{col 81}{space 3} .0227947
{txt}{space 23}50  {c |}{col 28}{res}{space 2} .0678809{col 40}{space 2} .0263439{col 51}{space 1}    2.58{col 60}{space 3}0.010{col 68}{space 4} .0162478{col 81}{space 3}  .119514
{txt}{space 23}51  {c |}{col 28}{res}{space 2} .1432944{col 40}{space 2} .0164285{col 51}{space 1}    8.72{col 60}{space 3}0.000{col 68}{space 4} .1110951{col 81}{space 3} .1754938
{txt}{space 23}52  {c |}{col 28}{res}{space 2} .1296246{col 40}{space 2} .0179309{col 51}{space 1}    7.23{col 60}{space 3}0.000{col 68}{space 4} .0944806{col 81}{space 3} .1647686
{txt}{space 26} {c |}
{space 22}year {c |}
{space 21}2003  {c |}{col 28}{res}{space 2} .0041487{col 40}{space 2} .0069612{col 51}{space 1}    0.60{col 60}{space 3}0.551{col 68}{space 4} -.009495{col 81}{space 3} .0177923
{txt}{space 21}2004  {c |}{col 28}{res}{space 2} .0013783{col 40}{space 2} .0071792{col 51}{space 1}    0.19{col 60}{space 3}0.848{col 68}{space 4}-.0126927{col 81}{space 3} .0154494
{txt}{space 21}2005  {c |}{col 28}{res}{space 2}-.0048227{col 40}{space 2} .0076558{col 51}{space 1}   -0.63{col 60}{space 3}0.529{col 68}{space 4}-.0198277{col 81}{space 3} .0101824
{txt}{space 21}2006  {c |}{col 28}{res}{space 2}-.0029042{col 40}{space 2} .0080804{col 51}{space 1}   -0.36{col 60}{space 3}0.719{col 68}{space 4}-.0187415{col 81}{space 3} .0129331
{txt}{space 21}2007  {c |}{col 28}{res}{space 2} .0000638{col 40}{space 2} .0085645{col 51}{space 1}    0.01{col 60}{space 3}0.994{col 68}{space 4}-.0167223{col 81}{space 3}   .01685
{txt}{space 21}2008  {c |}{col 28}{res}{space 2} .0001425{col 40}{space 2} .0089777{col 51}{space 1}    0.02{col 60}{space 3}0.987{col 68}{space 4}-.0174534{col 81}{space 3} .0177385
{txt}{space 21}2009  {c |}{col 28}{res}{space 2} .0110859{col 40}{space 2} .0117431{col 51}{space 1}    0.94{col 60}{space 3}0.345{col 68}{space 4}-.0119301{col 81}{space 3} .0341018
{txt}{space 21}2010  {c |}{col 28}{res}{space 2} .0517056{col 40}{space 2} .0126687{col 51}{space 1}    4.08{col 60}{space 3}0.000{col 68}{space 4} .0268754{col 81}{space 3} .0765358
{txt}{space 21}2011  {c |}{col 28}{res}{space 2} .0346411{col 40}{space 2} .0111962{col 51}{space 1}    3.09{col 60}{space 3}0.002{col 68}{space 4}  .012697{col 81}{space 3} .0565853
{txt}{space 21}2012  {c |}{col 28}{res}{space 2} .0302093{col 40}{space 2} .0116956{col 51}{space 1}    2.58{col 60}{space 3}0.010{col 68}{space 4} .0072863{col 81}{space 3} .0531323
{txt}{space 21}2013  {c |}{col 28}{res}{space 2} .0413658{col 40}{space 2} .0124906{col 51}{space 1}    3.31{col 60}{space 3}0.001{col 68}{space 4} .0168847{col 81}{space 3} .0658469
{txt}{space 21}2014  {c |}{col 28}{res}{space 2} .0553608{col 40}{space 2} .0134428{col 51}{space 1}    4.12{col 60}{space 3}0.000{col 68}{space 4} .0290135{col 81}{space 3} .0817081
{txt}{space 21}2015  {c |}{col 28}{res}{space 2} .0544399{col 40}{space 2} .0135552{col 51}{space 1}    4.02{col 60}{space 3}0.000{col 68}{space 4} .0278723{col 81}{space 3} .0810075
{txt}{space 21}2016  {c |}{col 28}{res}{space 2}  .058037{col 40}{space 2} .0141507{col 51}{space 1}    4.10{col 60}{space 3}0.000{col 68}{space 4} .0303021{col 81}{space 3} .0857719
{txt}{space 21}2017  {c |}{col 28}{res}{space 2} .0726768{col 40}{space 2} .0149365{col 51}{space 1}    4.87{col 60}{space 3}0.000{col 68}{space 4} .0434018{col 81}{space 3} .1019517
{txt}{space 21}2018  {c |}{col 28}{res}{space 2} .0703719{col 40}{space 2} .0162299{col 51}{space 1}    4.34{col 60}{space 3}0.000{col 68}{space 4} .0385619{col 81}{space 3}  .102182
{txt}{space 21}2019  {c |}{col 28}{res}{space 2} .0547618{col 40}{space 2} .0165474{col 51}{space 1}    3.31{col 60}{space 3}0.001{col 68}{space 4} .0223295{col 81}{space 3} .0871942
{txt}{space 21}2020  {c |}{col 28}{res}{space 2} .0594197{col 40}{space 2} .0204429{col 51}{space 1}    2.91{col 60}{space 3}0.004{col 68}{space 4} .0193524{col 81}{space 3}  .099487
{txt}{space 21}2021  {c |}{col 28}{res}{space 2} .2012786{col 40}{space 2} .0226158{col 51}{space 1}    8.90{col 60}{space 3}0.000{col 68}{space 4} .1569525{col 81}{space 3} .2456048
{txt}{space 21}2022  {c |}{col 28}{res}{space 2} .1751227{col 40}{space 2} .0228385{col 51}{space 1}    7.67{col 60}{space 3}0.000{col 68}{space 4}   .13036{col 81}{space 3} .2198853
{txt}{space 26} {c |}
{space 10}itmod_monthcount {c |}{col 28}{res}{space 2} -.000014{col 40}{space 2} .0002099{col 51}{space 1}   -0.07{col 60}{space 3}0.947{col 68}{space 4}-.0004254{col 81}{space 3} .0003974
{txt}{space 2}startcohort_2003_itstart {c |}{col 28}{res}{space 2}-.0439872{col 40}{space 2} .0319475{col 51}{space 1}   -1.38{col 60}{space 3}0.169{col 68}{space 4} -.106603{col 81}{space 3} .0186287
{txt}{space 2}startcohort_2004_itstart {c |}{col 28}{res}{space 2} .0121945{col 40}{space 2} .0155668{col 51}{space 1}    0.78{col 60}{space 3}0.433{col 68}{space 4} -.018316{col 81}{space 3} .0427049
{txt}{space 2}startcohort_2005_itstart {c |}{col 28}{res}{space 2} .1424245{col 40}{space 2} .0179017{col 51}{space 1}    7.96{col 60}{space 3}0.000{col 68}{space 4} .1073379{col 81}{space 3} .1775112
{txt}{space 2}startcohort_2006_itstart {c |}{col 28}{res}{space 2} .1056374{col 40}{space 2} .0192892{col 51}{space 1}    5.48{col 60}{space 3}0.000{col 68}{space 4} .0678313{col 81}{space 3} .1434436
{txt}{space 2}startcohort_2007_itstart {c |}{col 28}{res}{space 2}-.0320446{col 40}{space 2} .0221485{col 51}{space 1}   -1.45{col 60}{space 3}0.148{col 68}{space 4} -.075455{col 81}{space 3} .0113657
{txt}{space 2}startcohort_2008_itstart {c |}{col 28}{res}{space 2} .0053136{col 40}{space 2} .0226447{col 51}{space 1}    0.23{col 60}{space 3}0.814{col 68}{space 4}-.0390692{col 81}{space 3} .0496965
{txt}{space 2}startcohort_2009_itstart {c |}{col 28}{res}{space 2}-.0223294{col 40}{space 2} .0185139{col 51}{space 1}   -1.21{col 60}{space 3}0.228{col 68}{space 4}-.0586161{col 81}{space 3} .0139573
{txt}{space 2}startcohort_2010_itstart {c |}{col 28}{res}{space 2}-.0934506{col 40}{space 2} .0181674{col 51}{space 1}   -5.14{col 60}{space 3}0.000{col 68}{space 4} -.129058{col 81}{space 3}-.0578431
{txt}{space 2}startcohort_2012_itstart {c |}{col 28}{res}{space 2}-.1295609{col 40}{space 2} .0222239{col 51}{space 1}   -5.83{col 60}{space 3}0.000{col 68}{space 4} -.173119{col 81}{space 3}-.0860029
{txt}{space 2}startcohort_2013_itstart {c |}{col 28}{res}{space 2} -.045939{col 40}{space 2} .0210309{col 51}{space 1}   -2.18{col 60}{space 3}0.029{col 68}{space 4}-.0871588{col 81}{space 3}-.0047192
{txt}{space 2}startcohort_2014_itstart {c |}{col 28}{res}{space 2}-.1448707{col 40}{space 2} .0241514{col 51}{space 1}   -6.00{col 60}{space 3}0.000{col 68}{space 4}-.1922067{col 81}{space 3}-.0975348
{txt}{space 2}startcohort_2015_itstart {c |}{col 28}{res}{space 2}-.0079223{col 40}{space 2} .0217585{col 51}{space 1}   -0.36{col 60}{space 3}0.716{col 68}{space 4}-.0505682{col 81}{space 3} .0347235
{txt}{space 2}startcohort_2016_itstart {c |}{col 28}{res}{space 2} -.202721{col 40}{space 2} .0238574{col 51}{space 1}   -8.50{col 60}{space 3}0.000{col 68}{space 4}-.2494806{col 81}{space 3}-.1559614
{txt}{space 2}startcohort_2017_itstart {c |}{col 28}{res}{space 2}-.0324524{col 40}{space 2} .0237995{col 51}{space 1}   -1.36{col 60}{space 3}0.173{col 68}{space 4}-.0790984{col 81}{space 3} .0141937
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m4f if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m4f if e(sample), residuals
{res}{txt}(5,520 missing values generated)

{com}. 
. gen sse_m4f = predsy_m4f * predsy_m4f if e(sample)
{txt}(5,520 missing values generated)

{com}. gen ssr_m4f = residsy_m4f * residsy_m4f if e(sample)
{txt}(5,520 missing values generated)

{com}. 
. egen sum_sse_m4f = total(sse_m4f) if e(sample)
{txt}(5,519 missing values generated)

{com}. egen sum_ssr_m4f = total(ssr_m4f) if e(sample)
{txt}(5,519 missing values generated)

{com}. 
. gen r2_m4f = sum_ssr_m4f/(sum_sse_m4f + sum_ssr_m4f)
{txt}(5,519 missing values generated)

{com}. 
. sum r2_m4f

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}r2_m4f {c |}{res}      6,294    .2141671           0   .2141671   .2141671
{txt}
{com}. 
. 
. *
. *
. *
. 
. * [MODEL F4: TOTAL ERROR  RATE] FIGURE F3A:  UNCONDITIONAL ADAPTATION EFFECTS -- E(Y) [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:6,293}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .1728598{col 26}{space 2} .0097647{col 37}{space 1}   17.70{col 46}{space 3}0.000{col 54}{space 4} .1537213{col 67}{space 3} .1919984
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1834596{col 26}{space 2} .0066201{col 37}{space 1}   27.71{col 46}{space 3}0.000{col 54}{space 4} .1704845{col 67}{space 3} .1964347
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .2001852{col 26}{space 2} .0116585{col 37}{space 1}   17.17{col 46}{space 3}0.000{col 54}{space 4} .1773351{col 67}{space 3} .2230354
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1982761{col 26}{space 2} .0106572{col 37}{space 1}   18.60{col 46}{space 3}0.000{col 54}{space 4} .1773884{col 67}{space 3} .2191638
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1965778{col 26}{space 2} .0098969{col 37}{space 1}   19.86{col 46}{space 3}0.000{col 54}{space 4} .1771802{col 67}{space 3} .2159754
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1951357{col 26}{space 2} .0093516{col 37}{space 1}   20.87{col 46}{space 3}0.000{col 54}{space 4} .1768069{col 67}{space 3} .2134644
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .1939083{col 26}{space 2} .0089556{col 37}{space 1}   21.65{col 46}{space 3}0.000{col 54}{space 4} .1763556{col 67}{space 3} .2114609
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1928542{col 26}{space 2} .0086583{col 37}{space 1}   22.27{col 46}{space 3}0.000{col 54}{space 4} .1758842{col 67}{space 3} .2098241
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .1919319{col 26}{space 2}  .008428{col 37}{space 1}   22.77{col 46}{space 3}0.000{col 54}{space 4} .1754133{col 67}{space 3} .2084506
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .1911002{col 26}{space 2} .0082527{col 37}{space 1}   23.16{col 46}{space 3}0.000{col 54}{space 4} .1749253{col 67}{space 3} .2072752
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .1903175{col 26}{space 2} .0081372{col 37}{space 1}   23.39{col 46}{space 3}0.000{col 54}{space 4} .1743688{col 67}{space 3} .2062662
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .1895424{col 26}{space 2} .0080997{col 37}{space 1}   23.40{col 46}{space 3}0.000{col 54}{space 4} .1736672{col 67}{space 3} .2054176
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE F3A{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Total Program Error Rate [MODEL F4]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
> xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount_placebo}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F4.FIGURE F3A.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F4.FIGURE F3A.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F4.FIGURE F3A.04-10-2025.gph} saved

{com}. 
. *
. *
. *
. *
. 
. * [MODEL F4: TOTAL PROGRAM ERROR  RATE] FIGURE F3B:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (tot_interstate_catF2==2) & LOW COMPLEXITY (tot_interstate_catF2==0) VALUES [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]: ***
. 
. margins r.tot_interstate_catF2 if tot_interstate_catF2==0|tot_interstate_catF2==2, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,200}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 12}{hline 11}
{col 26}{text}{c |}         df{col 38}        chi2{col 50}     P>chi2
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 12}{hline 11}
tot_interstate_catF2@_at {c |}
{space 12}(2 vs 0)  1  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     3.71{col 50}{space 2}   0.0541
{txt}{space 12}(2 vs 0)  2  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.02{col 50}{space 2}   0.9022
{txt}{space 12}(2 vs 0)  3  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.91{col 50}{space 2}   0.3412
{txt}{space 12}(2 vs 0)  4  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.74{col 50}{space 2}   0.3910
{txt}{space 12}(2 vs 0)  5  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.50{col 50}{space 2}   0.4776
{txt}{space 12}(2 vs 0)  6  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.27{col 50}{space 2}   0.6019
{txt}{space 12}(2 vs 0)  7  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.12{col 50}{space 2}   0.7339
{txt}{space 12}(2 vs 0)  8  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.05{col 50}{space 2}   0.8301
{txt}{space 12}(2 vs 0)  9  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.03{col 50}{space 2}   0.8700
{txt}{space 12}(2 vs 0) 10  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.03{col 50}{space 2}   0.8539
{txt}{space 12}(2 vs 0) 11  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.07{col 50}{space 2}   0.7880
{txt}{space 12}(2 vs 0) 12  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.17{col 50}{space 2}   0.6788
{col 1}{text}                  Joint {col 26}{c |}{result}  (not testable)
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}   Contrast{col 38}   std. err.{col 50}     [95% con{col 63}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
tot_interstate_catF2@_at {c |}
{space 12}(2 vs 0)  1  {c |}{col 26}{res}{space 2}  .013311{col 38}{space 2} .0069117{col 49}{space 5}-.0002357{col 63}{space 3} .0268577
{txt}{space 12}(2 vs 0)  2  {c |}{col 26}{res}{space 2} .0010725{col 38}{space 2} .0087262{col 49}{space 5}-.0160306{col 63}{space 3} .0181756
{txt}{space 12}(2 vs 0)  3  {c |}{col 26}{res}{space 2}-.0166333{col 38}{space 2} .0174749{col 49}{space 5}-.0508836{col 63}{space 3} .0176169
{txt}{space 12}(2 vs 0)  4  {c |}{col 26}{res}{space 2}-.0119496{col 38}{space 2} .0139309{col 49}{space 5}-.0392537{col 63}{space 3} .0153544
{txt}{space 12}(2 vs 0)  5  {c |}{col 26}{res}{space 2}-.0081465{col 38}{space 2} .0114714{col 49}{space 5}-.0306302{col 63}{space 3} .0143371
{txt}{space 12}(2 vs 0)  6  {c |}{col 26}{res}{space 2} -.005269{col 38}{space 2} .0100992{col 49}{space 5}-.0250631{col 63}{space 3} .0145251
{txt}{space 12}(2 vs 0)  7  {c |}{col 26}{res}{space 2}-.0032573{col 38}{space 2} .0095816{col 49}{space 5}-.0220369{col 63}{space 3} .0155222
{txt}{space 12}(2 vs 0)  8  {c |}{col 26}{res}{space 2}-.0020519{col 38}{space 2} .0095653{col 49}{space 5}-.0207996{col 63}{space 3} .0166959
{txt}{space 12}(2 vs 0)  9  {c |}{col 26}{res}{space 2} -.001593{col 38}{space 2} .0097339{col 49}{space 5}-.0206711{col 63}{space 3} .0174852
{txt}{space 12}(2 vs 0) 10  {c |}{col 26}{res}{space 2}-.0018209{col 38}{space 2}  .009891{col 49}{space 5}-.0212069{col 63}{space 3}  .017565
{txt}{space 12}(2 vs 0) 11  {c |}{col 26}{res}{space 2}-.0026761{col 38}{space 2} .0099496{col 49}{space 5} -.022177{col 63}{space 3} .0168248
{txt}{space 12}(2 vs 0) 12  {c |}{col 26}{res}{space 2}-.0040988{col 38}{space 2} .0098974{col 49}{space 5}-.0234973{col 63}{space 3} .0152997
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE F3B{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Total Program Error Rate [MODEL F4]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount_placebo}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F4.FIGURE F3B.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F4.FIGURE F3B.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F4.FIGURE F3B.04-10-2025.gph} saved

{com}. *
. *
. *
. * [MODEL F4: TOTAL PROGRAM ERROR  RATE] FIGURE F3C:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (tot_diffoccupseek_catF2==2) & LOW COMPLEXITY (tot_extbenefits_catF2==0) VALUES [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]: ***
. 
. margins r.tot_diffoccupseek_catF2 if tot_diffoccupseek_catF2==0|tot_diffoccupseek_catF2==2, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,159}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 28}{c TT}{hline 11}{hline 12}{hline 11}
{col 29}{text}{c |}         df{col 41}        chi2{col 53}     P>chi2
{res}{col 1}{text}{hline 28}{c +}{hline 11}{hline 12}{hline 11}
tot_diffoccupseek_catF2@_at {c |}
{space 15}(2 vs 0)  1  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}    40.65{col 53}{space 2}   0.0000
{txt}{space 15}(2 vs 0)  2  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}    18.12{col 53}{space 2}   0.0000
{txt}{space 15}(2 vs 0)  3  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     2.62{col 53}{space 2}   0.1055
{txt}{space 15}(2 vs 0)  4  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     3.73{col 53}{space 2}   0.0534
{txt}{space 15}(2 vs 0)  5  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     5.16{col 53}{space 2}   0.0231
{txt}{space 15}(2 vs 0)  6  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     6.49{col 53}{space 2}   0.0108
{txt}{space 15}(2 vs 0)  7  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     7.15{col 53}{space 2}   0.0075
{txt}{space 15}(2 vs 0)  8  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     7.01{col 53}{space 2}   0.0081
{txt}{space 15}(2 vs 0)  9  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     6.44{col 53}{space 2}   0.0112
{txt}{space 15}(2 vs 0) 10  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     5.75{col 53}{space 2}   0.0165
{txt}{space 15}(2 vs 0) 11  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     5.10{col 53}{space 2}   0.0240
{txt}{space 15}(2 vs 0) 12  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     4.47{col 53}{space 2}   0.0346
{col 1}{text}                     Joint {col 29}{c |}{result}  (not testable)
{col 1}{text}{hline 28}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 29}{c |}{col 41} Delta-method
{col 29}{c |}   Contrast{col 41}   std. err.{col 53}     [95% con{col 66}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
tot_diffoccupseek_catF2@_at {c |}
{space 15}(2 vs 0)  1  {c |}{col 29}{res}{space 2}  .040877{col 41}{space 2} .0064116{col 52}{space 5} .0283105{col 66}{space 3} .0534436
{txt}{space 15}(2 vs 0)  2  {c |}{col 29}{res}{space 2}  .037212{col 41}{space 2} .0087408{col 52}{space 5} .0200804{col 66}{space 3} .0543437
{txt}{space 15}(2 vs 0)  3  {c |}{col 29}{res}{space 2} .0301675{col 41}{space 2} .0186371{col 52}{space 5}-.0063605{col 66}{space 3} .0666955
{txt}{space 15}(2 vs 0)  4  {c |}{col 29}{res}{space 2} .0287883{col 41}{space 2} .0149042{col 52}{space 5}-.0004233{col 66}{space 3}     .058
{txt}{space 15}(2 vs 0)  5  {c |}{col 29}{res}{space 2}  .027652{col 41}{space 2} .0121679{col 52}{space 5} .0038034{col 66}{space 3} .0515007
{txt}{space 15}(2 vs 0)  6  {c |}{col 29}{res}{space 2} .0266694{col 41}{space 2} .0104654{col 52}{space 5} .0061576{col 66}{space 3} .0471811
{txt}{space 15}(2 vs 0)  7  {c |}{col 29}{res}{space 2} .0257776{col 41}{space 2} .0096404{col 52}{space 5} .0068827{col 66}{space 3} .0446725
{txt}{space 15}(2 vs 0)  8  {c |}{col 29}{res}{space 2}  .024914{col 41}{space 2} .0094084{col 52}{space 5}  .006474{col 66}{space 3} .0433541
{txt}{space 15}(2 vs 0)  9  {c |}{col 29}{res}{space 2}  .024016{col 41}{space 2} .0094666{col 52}{space 5} .0054618{col 66}{space 3} .0425702
{txt}{space 15}(2 vs 0) 10  {c |}{col 29}{res}{space 2} .0230208{col 41}{space 2} .0095971{col 52}{space 5} .0042109{col 66}{space 3} .0418306
{txt}{space 15}(2 vs 0) 11  {c |}{col 29}{res}{space 2} .0218656{col 41}{space 2} .0096853{col 52}{space 5} .0028829{col 66}{space 3} .0408484
{txt}{space 15}(2 vs 0) 12  {c |}{col 29}{res}{space 2} .0204879{col 41}{space 2} .0096952{col 52}{space 5} .0014856{col 66}{space 3} .0394902
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE F3C{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Total Program Error Rate [MODEL F4]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount_placebo}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F4.FIGURE F3C.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F4.FIGURE F3C.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F4.FIGURE F3C.04-10-2025.gph} saved

{com}. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. 
. 
. *** TESTING H2 & H4: ABSOLUTE TYPE I PROGRAM ERROR RATE ORGANIZATIONAL ADAPTATION ['PLACEBO' INTERVENTION/TREATMENT MODELS] ***
. 
. 
. 
. *** ESTIMATE MODEL F5: TYPE I PROGRAM ERROR RATE [PROPORTION OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [ONLY STATE, YEAR, AND YEAR-ADOPTION COHORT UNIT EFFECTS] *** (FIGURES F4A-F4C) 
. 
. 
. npregress series t1error_rat  itmod_monthcount_placebo  i.t1_interstate_catF2    i.t1_diffoccupseek_catF2   if itmod_adopt_state==1, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real  benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat    i.stateid i.year    itmod_monthcount  startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart) vce(bootstrap, seed(123)  rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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done
{res}
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{txt}{space 23}28  {c |}{col 28}{res}{space 2} .0327048{col 40}{space 2} .0068736{col 51}{space 1}    4.76{col 60}{space 3}0.000{col 68}{space 4} .0192329{col 81}{space 3} .0461767
{txt}{space 23}29  {c |}{col 28}{res}{space 2} .0292075{col 40}{space 2} .0127205{col 51}{space 1}    2.30{col 60}{space 3}0.022{col 68}{space 4} .0042757{col 81}{space 3} .0541393
{txt}{space 23}33  {c |}{col 28}{res}{space 2} .0175456{col 40}{space 2} .0086167{col 51}{space 1}    2.04{col 60}{space 3}0.042{col 68}{space 4} .0006571{col 81}{space 3} .0344341
{txt}{space 23}38  {c |}{col 28}{res}{space 2} .0966851{col 40}{space 2} .0193448{col 51}{space 1}    5.00{col 60}{space 3}0.000{col 68}{space 4} .0587701{col 81}{space 3} .1346002
{txt}{space 23}40  {c |}{col 28}{res}{space 2}  .017309{col 40}{space 2} .0077304{col 51}{space 1}    2.24{col 60}{space 3}0.025{col 68}{space 4} .0021577{col 81}{space 3} .0324602
{txt}{space 23}42  {c |}{col 28}{res}{space 2} .0287548{col 40}{space 2} .0070255{col 51}{space 1}    4.09{col 60}{space 3}0.000{col 68}{space 4} .0149851{col 81}{space 3} .0425245
{txt}{space 23}46  {c |}{col 28}{res}{space 2}  .048889{col 40}{space 2} .0093499{col 51}{space 1}    5.23{col 60}{space 3}0.000{col 68}{space 4} .0305635{col 81}{space 3} .0672145
{txt}{space 23}47  {c |}{col 28}{res}{space 2}-.0090772{col 40}{space 2} .0111819{col 51}{space 1}   -0.81{col 60}{space 3}0.417{col 68}{space 4}-.0309935{col 81}{space 3}  .012839
{txt}{space 23}50  {c |}{col 28}{res}{space 2} .0183171{col 40}{space 2} .0139861{col 51}{space 1}    1.31{col 60}{space 3}0.190{col 68}{space 4}-.0090953{col 81}{space 3} .0457294
{txt}{space 23}51  {c |}{col 28}{res}{space 2}  .084795{col 40}{space 2} .0107561{col 51}{space 1}    7.88{col 60}{space 3}0.000{col 68}{space 4} .0637135{col 81}{space 3} .1058765
{txt}{space 23}52  {c |}{col 28}{res}{space 2} .0622059{col 40}{space 2} .0098631{col 51}{space 1}    6.31{col 60}{space 3}0.000{col 68}{space 4} .0428747{col 81}{space 3} .0815372
{txt}{space 26} {c |}
{space 22}year {c |}
{space 21}2003  {c |}{col 28}{res}{space 2}-.0022962{col 40}{space 2}  .003996{col 51}{space 1}   -0.57{col 60}{space 3}0.566{col 68}{space 4}-.0101283{col 81}{space 3} .0055359
{txt}{space 21}2004  {c |}{col 28}{res}{space 2}-.0131938{col 40}{space 2} .0039887{col 51}{space 1}   -3.31{col 60}{space 3}0.001{col 68}{space 4}-.0210116{col 81}{space 3} -.005376
{txt}{space 21}2005  {c |}{col 28}{res}{space 2}-.0153578{col 40}{space 2} .0042397{col 51}{space 1}   -3.62{col 60}{space 3}0.000{col 68}{space 4}-.0236676{col 81}{space 3}-.0070481
{txt}{space 21}2006  {c |}{col 28}{res}{space 2} -.017855{col 40}{space 2} .0048168{col 51}{space 1}   -3.71{col 60}{space 3}0.000{col 68}{space 4}-.0272957{col 81}{space 3}-.0084143
{txt}{space 21}2007  {c |}{col 28}{res}{space 2}-.0129349{col 40}{space 2}  .005396{col 51}{space 1}   -2.40{col 60}{space 3}0.017{col 68}{space 4}-.0235109{col 81}{space 3} -.002359
{txt}{space 21}2008  {c |}{col 28}{res}{space 2}-.0108116{col 40}{space 2} .0058544{col 51}{space 1}   -1.85{col 60}{space 3}0.065{col 68}{space 4} -.022286{col 81}{space 3} .0006628
{txt}{space 21}2009  {c |}{col 28}{res}{space 2} .0026678{col 40}{space 2} .0077154{col 51}{space 1}    0.35{col 60}{space 3}0.730{col 68}{space 4}-.0124541{col 81}{space 3} .0177896
{txt}{space 21}2010  {c |}{col 28}{res}{space 2} .0224353{col 40}{space 2} .0080658{col 51}{space 1}    2.78{col 60}{space 3}0.005{col 68}{space 4} .0066265{col 81}{space 3} .0382441
{txt}{space 21}2011  {c |}{col 28}{res}{space 2} .0056849{col 40}{space 2} .0075591{col 51}{space 1}    0.75{col 60}{space 3}0.452{col 68}{space 4}-.0091306{col 81}{space 3} .0205004
{txt}{space 21}2012  {c |}{col 28}{res}{space 2} .0114539{col 40}{space 2} .0076451{col 51}{space 1}    1.50{col 60}{space 3}0.134{col 68}{space 4}-.0035302{col 81}{space 3} .0264381
{txt}{space 21}2013  {c |}{col 28}{res}{space 2} .0031621{col 40}{space 2} .0079923{col 51}{space 1}    0.40{col 60}{space 3}0.692{col 68}{space 4}-.0125025{col 81}{space 3} .0188267
{txt}{space 21}2014  {c |}{col 28}{res}{space 2} .0149339{col 40}{space 2} .0089862{col 51}{space 1}    1.66{col 60}{space 3}0.097{col 68}{space 4}-.0026787{col 81}{space 3} .0325466
{txt}{space 21}2015  {c |}{col 28}{res}{space 2} .0261892{col 40}{space 2} .0100572{col 51}{space 1}    2.60{col 60}{space 3}0.009{col 68}{space 4} .0064774{col 81}{space 3}  .045901
{txt}{space 21}2016  {c |}{col 28}{res}{space 2} .0312456{col 40}{space 2} .0108065{col 51}{space 1}    2.89{col 60}{space 3}0.004{col 68}{space 4} .0100653{col 81}{space 3} .0524259
{txt}{space 21}2017  {c |}{col 28}{res}{space 2} .0386376{col 40}{space 2} .0112837{col 51}{space 1}    3.42{col 60}{space 3}0.001{col 68}{space 4} .0165218{col 81}{space 3} .0607533
{txt}{space 21}2018  {c |}{col 28}{res}{space 2}   .03194{col 40}{space 2} .0120465{col 51}{space 1}    2.65{col 60}{space 3}0.008{col 68}{space 4} .0083292{col 81}{space 3} .0555508
{txt}{space 21}2019  {c |}{col 28}{res}{space 2} .0272704{col 40}{space 2} .0122015{col 51}{space 1}    2.23{col 60}{space 3}0.025{col 68}{space 4} .0033558{col 81}{space 3}  .051185
{txt}{space 21}2020  {c |}{col 28}{res}{space 2} .0797147{col 40}{space 2} .0140501{col 51}{space 1}    5.67{col 60}{space 3}0.000{col 68}{space 4}  .052177{col 81}{space 3} .1072525
{txt}{space 21}2021  {c |}{col 28}{res}{space 2} .1397785{col 40}{space 2} .0156309{col 51}{space 1}    8.94{col 60}{space 3}0.000{col 68}{space 4} .1091424{col 81}{space 3} .1704146
{txt}{space 21}2022  {c |}{col 28}{res}{space 2}  .101468{col 40}{space 2} .0162904{col 51}{space 1}    6.23{col 60}{space 3}0.000{col 68}{space 4} .0695394{col 81}{space 3} .1333966
{txt}{space 26} {c |}
{space 10}itmod_monthcount {c |}{col 28}{res}{space 2}-.0001632{col 40}{space 2} .0001518{col 51}{space 1}   -1.08{col 60}{space 3}0.282{col 68}{space 4}-.0004607{col 81}{space 3} .0001343
{txt}{space 2}startcohort_2003_itstart {c |}{col 28}{res}{space 2}-.0196564{col 40}{space 2} .0176146{col 51}{space 1}   -1.12{col 60}{space 3}0.264{col 68}{space 4}-.0541804{col 81}{space 3} .0148676
{txt}{space 2}startcohort_2004_itstart {c |}{col 28}{res}{space 2}  .024162{col 40}{space 2} .0101888{col 51}{space 1}    2.37{col 60}{space 3}0.018{col 68}{space 4} .0041923{col 81}{space 3} .0441316
{txt}{space 2}startcohort_2005_itstart {c |}{col 28}{res}{space 2} .0729611{col 40}{space 2} .0123491{col 51}{space 1}    5.91{col 60}{space 3}0.000{col 68}{space 4} .0487573{col 81}{space 3} .0971648
{txt}{space 2}startcohort_2006_itstart {c |}{col 28}{res}{space 2} .0048698{col 40}{space 2} .0096327{col 51}{space 1}    0.51{col 60}{space 3}0.613{col 68}{space 4}  -.01401{col 81}{space 3} .0237496
{txt}{space 2}startcohort_2007_itstart {c |}{col 28}{res}{space 2}-.0435839{col 40}{space 2} .0124296{col 51}{space 1}   -3.51{col 60}{space 3}0.000{col 68}{space 4}-.0679454{col 81}{space 3}-.0192225
{txt}{space 2}startcohort_2008_itstart {c |}{col 28}{res}{space 2} .0077975{col 40}{space 2} .0142104{col 51}{space 1}    0.55{col 60}{space 3}0.583{col 68}{space 4}-.0200543{col 81}{space 3} .0356494
{txt}{space 2}startcohort_2009_itstart {c |}{col 28}{res}{space 2}-.0337393{col 40}{space 2} .0110644{col 51}{space 1}   -3.05{col 60}{space 3}0.002{col 68}{space 4}-.0554251{col 81}{space 3}-.0120535
{txt}{space 2}startcohort_2010_itstart {c |}{col 28}{res}{space 2}-.0548766{col 40}{space 2} .0108589{col 51}{space 1}   -5.05{col 60}{space 3}0.000{col 68}{space 4}-.0761596{col 81}{space 3}-.0335937
{txt}{space 2}startcohort_2012_itstart {c |}{col 28}{res}{space 2}-.0722453{col 40}{space 2} .0125442{col 51}{space 1}   -5.76{col 60}{space 3}0.000{col 68}{space 4}-.0968314{col 81}{space 3}-.0476591
{txt}{space 2}startcohort_2013_itstart {c |}{col 28}{res}{space 2}-.0644599{col 40}{space 2} .0125242{col 51}{space 1}   -5.15{col 60}{space 3}0.000{col 68}{space 4}-.0890068{col 81}{space 3}-.0399129
{txt}{space 2}startcohort_2014_itstart {c |}{col 28}{res}{space 2}-.0558436{col 40}{space 2} .0147025{col 51}{space 1}   -3.80{col 60}{space 3}0.000{col 68}{space 4}  -.08466{col 81}{space 3}-.0270272
{txt}{space 2}startcohort_2015_itstart {c |}{col 28}{res}{space 2}-.0568597{col 40}{space 2}  .012907{col 51}{space 1}   -4.41{col 60}{space 3}0.000{col 68}{space 4} -.082157{col 81}{space 3}-.0315624
{txt}{space 2}startcohort_2016_itstart {c |}{col 28}{res}{space 2}-.1239312{col 40}{space 2} .0153509{col 51}{space 1}   -8.07{col 60}{space 3}0.000{col 68}{space 4}-.1540184{col 81}{space 3} -.093844
{txt}{space 2}startcohort_2017_itstart {c |}{col 28}{res}{space 2}-.0139629{col 40}{space 2} .0180421{col 51}{space 1}   -0.77{col 60}{space 3}0.439{col 68}{space 4}-.0493247{col 81}{space 3}  .021399
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m5f if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m5f if e(sample), residuals
{res}{txt}(5,280 missing values generated)

{com}. 
. gen sse_m5f = predsy_m5f * predsy_m5f if e(sample)
{txt}(5,280 missing values generated)

{com}. gen ssr_m5f = residsy_m5f * residsy_m5f if e(sample)
{txt}(5,280 missing values generated)

{com}. 
. egen sum_sse_m5f = total(sse_m5f) if e(sample)
{txt}(5,280 missing values generated)

{com}. egen sum_ssr_m5f = total(ssr_m5f) if e(sample)
{txt}(5,280 missing values generated)

{com}. 
. gen r2_m5f = sum_ssr_m5f/(sum_sse_m5f + sum_ssr_m5f)
{txt}(5,280 missing values generated)

{com}. 
. sum r2_m5f

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}r2_m5f {c |}{res}      6,533    .4480633           0   .4480633   .4480633
{txt}
{com}. 
. 
. 
. *
. *
. *
. 
. * [MODEL F5: ABSOLUTE TYPE I PROGRAM ERROR RATE] FIGURE F2A: UNCONDITIONAL ADAPTATION EFFECTS -- E(Y) [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:6,533}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0633236{col 26}{space 2} .0072786{col 37}{space 1}    8.70{col 46}{space 3}0.000{col 54}{space 4} .0490578{col 67}{space 3} .0775893
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0704951{col 26}{space 2} .0055994{col 37}{space 1}   12.59{col 46}{space 3}0.000{col 54}{space 4} .0595205{col 67}{space 3} .0814696
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0870564{col 26}{space 2} .0074395{col 37}{space 1}   11.70{col 46}{space 3}0.000{col 54}{space 4} .0724752{col 67}{space 3} .1016376
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0843251{col 26}{space 2} .0076446{col 37}{space 1}   11.03{col 46}{space 3}0.000{col 54}{space 4} .0693419{col 67}{space 3} .0993082
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0791041{col 26}{space 2} .0070811{col 37}{space 1}   11.17{col 46}{space 3}0.000{col 54}{space 4} .0652254{col 67}{space 3} .0929828
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0744888{col 26}{space 2} .0066389{col 37}{space 1}   11.22{col 46}{space 3}0.000{col 54}{space 4} .0614768{col 67}{space 3} .0875008
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .0704335{col 26}{space 2} .0062819{col 37}{space 1}   11.21{col 46}{space 3}0.000{col 54}{space 4} .0581212{col 67}{space 3} .0827459
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0668928{col 26}{space 2} .0059843{col 37}{space 1}   11.18{col 46}{space 3}0.000{col 54}{space 4} .0551638{col 67}{space 3} .0786218
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0638211{col 26}{space 2} .0057326{col 37}{space 1}   11.13{col 46}{space 3}0.000{col 54}{space 4} .0525855{col 67}{space 3} .0750567
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0611729{col 26}{space 2} .0055261{col 37}{space 1}   11.07{col 46}{space 3}0.000{col 54}{space 4} .0503419{col 67}{space 3} .0720039
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .0589027{col 26}{space 2} .0053762{col 37}{space 1}   10.96{col 46}{space 3}0.000{col 54}{space 4} .0483655{col 67}{space 3} .0694398
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .0569648{col 26}{space 2} .0053033{col 37}{space 1}   10.74{col 46}{space 3}0.000{col 54}{space 4} .0465705{col 67}{space 3} .0673592
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE F4A{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Absolute Type I Program Error Rate [MODEL F5]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
> xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount_placebo}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F5.FIGURE F4A.04-10-2024.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F5.FIGURE F4A.04-10-2024.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F5.FIGURE F4A.04-10-2024.gph} saved

{com}. 
. 
. *
. *
. *
. *
. 
. * [MODEL F5: ABSOLUTE TYPE I PROGRAM ERROR RATE] FIGURE F2B:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (t1_interstate_catF2==2) & LOW COMPLEXITY (t1_interstate_catF2==0) VALUES [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]: ***
. 
. margins r.t1_interstate_catF2 if t1_interstate_catF2==0|t1_interstate_catF2==2, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,390}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 24}{c TT}{hline 11}{hline 12}{hline 11}
{col 25}{text}{c |}         df{col 37}        chi2{col 49}     P>chi2
{res}{col 1}{text}{hline 24}{c +}{hline 11}{hline 12}{hline 11}
t1_interstate_catF2@_at {c |}
{space 11}(2 vs 0)  1  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     1.34{col 49}{space 2}   0.2463
{txt}{space 11}(2 vs 0)  2  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.69{col 49}{space 2}   0.4073
{txt}{space 11}(2 vs 0)  3  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.04{col 49}{space 2}   0.8321
{txt}{space 11}(2 vs 0)  4  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.21{col 49}{space 2}   0.6492
{txt}{space 11}(2 vs 0)  5  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.66{col 49}{space 2}   0.4179
{txt}{space 11}(2 vs 0)  6  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     1.24{col 49}{space 2}   0.2651
{txt}{space 11}(2 vs 0)  7  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     1.61{col 49}{space 2}   0.2046
{txt}{space 11}(2 vs 0)  8  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     1.58{col 49}{space 2}   0.2091
{txt}{space 11}(2 vs 0)  9  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     1.27{col 49}{space 2}   0.2590
{txt}{space 11}(2 vs 0) 10  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.87{col 49}{space 2}   0.3512
{txt}{space 11}(2 vs 0) 11  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.47{col 49}{space 2}   0.4927
{txt}{space 11}(2 vs 0) 12  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.15{col 49}{space 2}   0.6953
{col 1}{text}                 Joint {col 25}{c |}{result}  (not testable)
{col 1}{text}{hline 24}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 25}{c |}{col 37} Delta-method
{col 25}{c |}   Contrast{col 37}   std. err.{col 49}     [95% con{col 62}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
t1_interstate_catF2@_at {c |}
{space 11}(2 vs 0)  1  {c |}{col 25}{res}{space 2} .0048559{col 37}{space 2} .0041885{col 48}{space 5}-.0033534{col 62}{space 3} .0130652
{txt}{space 11}(2 vs 0)  2  {c |}{col 25}{res}{space 2} .0038275{col 37}{space 2} .0046191{col 48}{space 5}-.0052257{col 62}{space 3} .0128807
{txt}{space 11}(2 vs 0)  3  {c |}{col 25}{res}{space 2} .0022745{col 37}{space 2} .0107276{col 48}{space 5}-.0187511{col 62}{space 3} .0233002
{txt}{space 11}(2 vs 0)  4  {c |}{col 25}{res}{space 2} .0043273{col 37}{space 2} .0095127{col 48}{space 5}-.0143172{col 62}{space 3} .0229718
{txt}{space 11}(2 vs 0)  5  {c |}{col 25}{res}{space 2} .0062215{col 37}{space 2} .0076797{col 48}{space 5}-.0088303{col 62}{space 3} .0212734
{txt}{space 11}(2 vs 0)  6  {c |}{col 25}{res}{space 2} .0073845{col 37}{space 2} .0066261{col 48}{space 5}-.0056024{col 62}{space 3} .0203714
{txt}{space 11}(2 vs 0)  7  {c |}{col 25}{res}{space 2} .0078783{col 37}{space 2} .0062107{col 48}{space 5}-.0042945{col 62}{space 3} .0200511
{txt}{space 11}(2 vs 0)  8  {c |}{col 25}{res}{space 2}  .007765{col 37}{space 2} .0061818{col 48}{space 5}-.0043511{col 62}{space 3} .0198811
{txt}{space 11}(2 vs 0)  9  {c |}{col 25}{res}{space 2} .0071067{col 37}{space 2} .0062965{col 48}{space 5}-.0052342{col 62}{space 3} .0194476
{txt}{space 11}(2 vs 0) 10  {c |}{col 25}{res}{space 2} .0059654{col 37}{space 2} .0063993{col 48}{space 5} -.006577{col 62}{space 3} .0185079
{txt}{space 11}(2 vs 0) 11  {c |}{col 25}{res}{space 2} .0044033{col 37}{space 2} .0064183{col 48}{space 5}-.0081763{col 62}{space 3}  .016983
{txt}{space 11}(2 vs 0) 12  {c |}{col 25}{res}{space 2} .0024825{col 37}{space 2} .0063372{col 48}{space 5}-.0099382{col 62}{space 3} .0149032
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE F4B{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Absolute Type I Program Error Rate [MODEL F5]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount_placebo}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F5.FIGURE F4B.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F5.FIGURE F4B.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F5.FIGURE F4B.04-10-2025.gph} saved

{com}. *
. *
. *
. *
. *
. 
. * [MODEL F5: ABSOLUTE TYPE I PROGRAM ERROR RATE] FIGURE F2C:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (t1_diffoccupseek_catF2==2) & LOW COMPLEXITY (t1_diffoccupseek_catF2==0) VALUES [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]: ***
. 
. margins r.t1_diffoccupseek_catF2 if t1_diffoccupseek_catF2==0|t1_diffoccupseek_catF2==2, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,322}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 27}{c TT}{hline 11}{hline 12}{hline 11}
{col 28}{text}{c |}         df{col 40}        chi2{col 52}     P>chi2
{res}{col 1}{text}{hline 27}{c +}{hline 11}{hline 12}{hline 11}
t1_diffoccupseek_catF2@_at {c |}
{space 14}(2 vs 0)  1  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}    16.19{col 52}{space 2}   0.0001
{txt}{space 14}(2 vs 0)  2  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     6.04{col 52}{space 2}   0.0140
{txt}{space 14}(2 vs 0)  3  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.21{col 52}{space 2}   0.6461
{txt}{space 14}(2 vs 0)  4  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.28{col 52}{space 2}   0.5941
{txt}{space 14}(2 vs 0)  5  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.14{col 52}{space 2}   0.7036
{txt}{space 14}(2 vs 0)  6  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.03{col 52}{space 2}   0.8608
{txt}{space 14}(2 vs 0)  7  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.00{col 52}{space 2}   0.9697
{txt}{space 14}(2 vs 0)  8  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.05{col 52}{space 2}   0.8303
{txt}{space 14}(2 vs 0)  9  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.12{col 52}{space 2}   0.7325
{txt}{space 14}(2 vs 0) 10  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.18{col 52}{space 2}   0.6676
{txt}{space 14}(2 vs 0) 11  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.24{col 52}{space 2}   0.6252
{txt}{space 14}(2 vs 0) 12  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.28{col 52}{space 2}   0.5983
{col 1}{text}                    Joint {col 28}{c |}{result}  (not testable)
{col 1}{text}{hline 27}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 28}{c |}{col 40} Delta-method
{col 28}{c |}   Contrast{col 40}   std. err.{col 52}     [95% con{col 65}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
t1_diffoccupseek_catF2@_at {c |}
{space 14}(2 vs 0)  1  {c |}{col 28}{res}{space 2} .0161723{col 40}{space 2} .0040199{col 51}{space 5} .0082936{col 65}{space 3} .0240511
{txt}{space 14}(2 vs 0)  2  {c |}{col 28}{res}{space 2} .0102759{col 40}{space 2} .0041818{col 51}{space 5} .0020797{col 65}{space 3} .0184721
{txt}{space 14}(2 vs 0)  3  {c |}{col 28}{res}{space 2}-.0044001{col 40}{space 2}  .009582{col 51}{space 5}-.0231804{col 65}{space 3} .0143802
{txt}{space 14}(2 vs 0)  4  {c |}{col 28}{res}{space 2} -.004587{col 40}{space 2} .0086067{col 51}{space 5}-.0214558{col 65}{space 3} .0122819
{txt}{space 14}(2 vs 0)  5  {c |}{col 28}{res}{space 2}-.0026935{col 40}{space 2} .0070794{col 51}{space 5}-.0165689{col 65}{space 3} .0111818
{txt}{space 14}(2 vs 0)  6  {c |}{col 28}{res}{space 2}-.0010957{col 40}{space 2} .0062496{col 51}{space 5}-.0133447{col 65}{space 3} .0111534
{txt}{space 14}(2 vs 0)  7  {c |}{col 28}{res}{space 2} .0002269{col 40}{space 2} .0059793{col 51}{space 5}-.0114925{col 65}{space 3} .0119462
{txt}{space 14}(2 vs 0)  8  {c |}{col 28}{res}{space 2} .0012942{col 40}{space 2} .0060402{col 51}{space 5}-.0105443{col 65}{space 3} .0131328
{txt}{space 14}(2 vs 0)  9  {c |}{col 28}{res}{space 2} .0021267{col 40}{space 2} .0062219{col 51}{space 5}-.0100679{col 65}{space 3} .0143214
{txt}{space 14}(2 vs 0) 10  {c |}{col 28}{res}{space 2} .0027445{col 40}{space 2} .0063913{col 51}{space 5}-.0097822{col 65}{space 3} .0152713
{txt}{space 14}(2 vs 0) 11  {c |}{col 28}{res}{space 2} .0031679{col 40}{space 2}  .006485{col 51}{space 5}-.0095426{col 65}{space 3} .0158783
{txt}{space 14}(2 vs 0) 12  {c |}{col 28}{res}{space 2}  .003417{col 40}{space 2} .0064851{col 51}{space 5}-.0092935{col 65}{space 3} .0161275
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE F4C{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Absolute Type I Program Error Rate [MODEL F5]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount_placebo}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F5.FIGURE F4C.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F5.FIGURE F4C.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F5.FIGURE F4C.04-10-2025.gph} saved

{com}. 
. 
. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
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. *** TESTING H2 & H4: RELATIVE TYPE I PROGRAM ERROR RATE [TYPE I ERROR RATE / (TYPE I ERROR RATE + TYPE II ERROR RATE)] ORGANIZATIONAL ADAPTATION  ['PLACEBO' INTERVENTION/TREATMENT MODELS]***
. 
. 
. 
. 
. *** ESTIMATE MODEL F6: RELATIVE TYPE I PROGRAM ERROR RATE [WITH ADDITIONAL COVARIATES: PROPORTION OF SAMPLE-WEIGHTED CASES OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [ONLY STATE, YEAR, AND YEAR-ADOPTION COHORT UNIT EFFECTS] ***  (FIGURES F4D-F4F) 
. 
. 
. 
. npregress series relt1error_rat   itmod_monthcount_placebo   i.relt1_interstate_catF2   i.relt1_diffoccupseek_catF2   if itmod_adopt_state==1, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  itmod_monthcount  startcohort_2003_itstart startcohort_2004_itstart startcohort_2005_itstart   startcohort_2006_itstart  startcohort_2007_itstart  startcohort_2008_itstart  startcohort_2009_itstart  startcohort_2010_itstart  startcohort_2012_itstart  startcohort_2013_itstart  startcohort_2014_itstart  startcohort_2015_itstart  startcohort_2016_itstart startcohort_2017_itstart)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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{txt}{space 23}28  {c |}{col 28}{res}{space 2}-.2092712{col 40}{space 2} .0332333{col 51}{space 1}   -6.30{col 60}{space 3}0.000{col 68}{space 4}-.2744072{col 81}{space 3}-.1441352
{txt}{space 23}29  {c |}{col 28}{res}{space 2}-.1642705{col 40}{space 2} .0446814{col 51}{space 1}   -3.68{col 60}{space 3}0.000{col 68}{space 4}-.2518444{col 81}{space 3}-.0766966
{txt}{space 23}33  {c |}{col 28}{res}{space 2}-.0237531{col 40}{space 2} .0352416{col 51}{space 1}   -0.67{col 60}{space 3}0.500{col 68}{space 4}-.0928254{col 81}{space 3} .0453192
{txt}{space 23}38  {c |}{col 28}{res}{space 2}-.0270526{col 40}{space 2} .0521139{col 51}{space 1}   -0.52{col 60}{space 3}0.604{col 68}{space 4} -.129194{col 81}{space 3} .0750887
{txt}{space 23}40  {c |}{col 28}{res}{space 2} -.081367{col 40}{space 2} .0333221{col 51}{space 1}   -2.44{col 60}{space 3}0.015{col 68}{space 4}-.1466772{col 81}{space 3}-.0160569
{txt}{space 23}42  {c |}{col 28}{res}{space 2}-.1412648{col 40}{space 2} .0303278{col 51}{space 1}   -4.66{col 60}{space 3}0.000{col 68}{space 4}-.2007062{col 81}{space 3}-.0818235
{txt}{space 23}46  {c |}{col 28}{res}{space 2} .0320838{col 40}{space 2} .0349442{col 51}{space 1}    0.92{col 60}{space 3}0.359{col 68}{space 4}-.0364055{col 81}{space 3} .1005732
{txt}{space 23}47  {c |}{col 28}{res}{space 2}-.1876628{col 40}{space 2} .0422604{col 51}{space 1}   -4.44{col 60}{space 3}0.000{col 68}{space 4}-.2704916{col 81}{space 3} -.104834
{txt}{space 23}50  {c |}{col 28}{res}{space 2}-.0489358{col 40}{space 2} .0528779{col 51}{space 1}   -0.93{col 60}{space 3}0.355{col 68}{space 4}-.1525746{col 81}{space 3}  .054703
{txt}{space 23}51  {c |}{col 28}{res}{space 2} .0544162{col 40}{space 2} .0382281{col 51}{space 1}    1.42{col 60}{space 3}0.155{col 68}{space 4}-.0205095{col 81}{space 3} .1293419
{txt}{space 23}52  {c |}{col 28}{res}{space 2}-.0356726{col 40}{space 2} .0407779{col 51}{space 1}   -0.87{col 60}{space 3}0.382{col 68}{space 4}-.1155958{col 81}{space 3} .0442505
{txt}{space 26} {c |}
{space 22}year {c |}
{space 21}2003  {c |}{col 28}{res}{space 2} .0100794{col 40}{space 2} .0227081{col 51}{space 1}    0.44{col 60}{space 3}0.657{col 68}{space 4}-.0344276{col 81}{space 3} .0545864
{txt}{space 21}2004  {c |}{col 28}{res}{space 2}-.0545422{col 40}{space 2} .0223682{col 51}{space 1}   -2.44{col 60}{space 3}0.015{col 68}{space 4}-.0983831{col 81}{space 3}-.0107013
{txt}{space 21}2005  {c |}{col 28}{res}{space 2}-.0471541{col 40}{space 2} .0233451{col 51}{space 1}   -2.02{col 60}{space 3}0.043{col 68}{space 4}-.0929097{col 81}{space 3}-.0013984
{txt}{space 21}2006  {c |}{col 28}{res}{space 2}-.0537567{col 40}{space 2} .0244033{col 51}{space 1}   -2.20{col 60}{space 3}0.028{col 68}{space 4}-.1015863{col 81}{space 3} -.005927
{txt}{space 21}2007  {c |}{col 28}{res}{space 2}-.0528236{col 40}{space 2} .0253555{col 51}{space 1}   -2.08{col 60}{space 3}0.037{col 68}{space 4}-.1025194{col 81}{space 3}-.0031278
{txt}{space 21}2008  {c |}{col 28}{res}{space 2}-.0575413{col 40}{space 2} .0249466{col 51}{space 1}   -2.31{col 60}{space 3}0.021{col 68}{space 4}-.1064357{col 81}{space 3}-.0086469
{txt}{space 21}2009  {c |}{col 28}{res}{space 2} .0212638{col 40}{space 2} .0283766{col 51}{space 1}    0.75{col 60}{space 3}0.454{col 68}{space 4}-.0343533{col 81}{space 3} .0768808
{txt}{space 21}2010  {c |}{col 28}{res}{space 2}  .038272{col 40}{space 2} .0286764{col 51}{space 1}    1.33{col 60}{space 3}0.182{col 68}{space 4}-.0179327{col 81}{space 3} .0944768
{txt}{space 21}2011  {c |}{col 28}{res}{space 2}  .003579{col 40}{space 2} .0289559{col 51}{space 1}    0.12{col 60}{space 3}0.902{col 68}{space 4}-.0531735{col 81}{space 3} .0603314
{txt}{space 21}2012  {c |}{col 28}{res}{space 2} .0225193{col 40}{space 2} .0289529{col 51}{space 1}    0.78{col 60}{space 3}0.437{col 68}{space 4}-.0342274{col 81}{space 3}  .079266
{txt}{space 21}2013  {c |}{col 28}{res}{space 2}-.0352488{col 40}{space 2} .0311603{col 51}{space 1}   -1.13{col 60}{space 3}0.258{col 68}{space 4}-.0963218{col 81}{space 3} .0258242
{txt}{space 21}2014  {c |}{col 28}{res}{space 2}-.0267509{col 40}{space 2} .0308646{col 51}{space 1}   -0.87{col 60}{space 3}0.386{col 68}{space 4}-.0872445{col 81}{space 3} .0337426
{txt}{space 21}2015  {c |}{col 28}{res}{space 2} .0099341{col 40}{space 2} .0342172{col 51}{space 1}    0.29{col 60}{space 3}0.772{col 68}{space 4}-.0571303{col 81}{space 3} .0769985
{txt}{space 21}2016  {c |}{col 28}{res}{space 2} .0079498{col 40}{space 2} .0340082{col 51}{space 1}    0.23{col 60}{space 3}0.815{col 68}{space 4}-.0587051{col 81}{space 3} .0746046
{txt}{space 21}2017  {c |}{col 28}{res}{space 2} .0023354{col 40}{space 2} .0367549{col 51}{space 1}    0.06{col 60}{space 3}0.949{col 68}{space 4}-.0697029{col 81}{space 3} .0743737
{txt}{space 21}2018  {c |}{col 28}{res}{space 2}-.0077903{col 40}{space 2} .0400156{col 51}{space 1}   -0.19{col 60}{space 3}0.846{col 68}{space 4}-.0862194{col 81}{space 3} .0706387
{txt}{space 21}2019  {c |}{col 28}{res}{space 2}-.0371977{col 40}{space 2} .0414676{col 51}{space 1}   -0.90{col 60}{space 3}0.370{col 68}{space 4}-.1184727{col 81}{space 3} .0440773
{txt}{space 21}2020  {c |}{col 28}{res}{space 2} .1126702{col 40}{space 2} .0485228{col 51}{space 1}    2.32{col 60}{space 3}0.020{col 68}{space 4} .0175673{col 81}{space 3}  .207773
{txt}{space 21}2021  {c |}{col 28}{res}{space 2} .2615463{col 40}{space 2} .0490959{col 51}{space 1}    5.33{col 60}{space 3}0.000{col 68}{space 4} .1653202{col 81}{space 3} .3577725
{txt}{space 21}2022  {c |}{col 28}{res}{space 2} .1440312{col 40}{space 2} .0514548{col 51}{space 1}    2.80{col 60}{space 3}0.005{col 68}{space 4} .0431816{col 81}{space 3} .2448809
{txt}{space 26} {c |}
{space 10}itmod_monthcount {c |}{col 28}{res}{space 2}-.0012028{col 40}{space 2} .0003724{col 51}{space 1}   -3.23{col 60}{space 3}0.001{col 68}{space 4}-.0019327{col 81}{space 3}-.0004729
{txt}{space 2}startcohort_2003_itstart {c |}{col 28}{res}{space 2} .0293955{col 40}{space 2} .0738096{col 51}{space 1}    0.40{col 60}{space 3}0.690{col 68}{space 4}-.1152686{col 81}{space 3} .1740596
{txt}{space 2}startcohort_2004_itstart {c |}{col 28}{res}{space 2} .0030076{col 40}{space 2}   .06412{col 51}{space 1}    0.05{col 60}{space 3}0.963{col 68}{space 4}-.1226651{col 81}{space 3} .1286804
{txt}{space 2}startcohort_2005_itstart {c |}{col 28}{res}{space 2}-.0756251{col 40}{space 2} .0459857{col 51}{space 1}   -1.64{col 60}{space 3}0.100{col 68}{space 4}-.1657554{col 81}{space 3} .0145051
{txt}{space 2}startcohort_2006_itstart {c |}{col 28}{res}{space 2} .0272389{col 40}{space 2} .0372691{col 51}{space 1}    0.73{col 60}{space 3}0.465{col 68}{space 4}-.0458071{col 81}{space 3} .1002849
{txt}{space 2}startcohort_2007_itstart {c |}{col 28}{res}{space 2}-.0966019{col 40}{space 2} .0517224{col 51}{space 1}   -1.87{col 60}{space 3}0.062{col 68}{space 4}-.1979759{col 81}{space 3} .0047721
{txt}{space 2}startcohort_2008_itstart {c |}{col 28}{res}{space 2}-.0620623{col 40}{space 2}  .064355{col 51}{space 1}   -0.96{col 60}{space 3}0.335{col 68}{space 4}-.1881958{col 81}{space 3} .0640713
{txt}{space 2}startcohort_2009_itstart {c |}{col 28}{res}{space 2}-.1425003{col 40}{space 2} .0502795{col 51}{space 1}   -2.83{col 60}{space 3}0.005{col 68}{space 4}-.2410464{col 81}{space 3}-.0439542
{txt}{space 2}startcohort_2010_itstart {c |}{col 28}{res}{space 2} .0132146{col 40}{space 2} .0443048{col 51}{space 1}    0.30{col 60}{space 3}0.766{col 68}{space 4}-.0736213{col 81}{space 3} .1000504
{txt}{space 2}startcohort_2012_itstart {c |}{col 28}{res}{space 2}-.0818684{col 40}{space 2} .0497758{col 51}{space 1}   -1.64{col 60}{space 3}0.100{col 68}{space 4}-.1794272{col 81}{space 3} .0156903
{txt}{space 2}startcohort_2013_itstart {c |}{col 28}{res}{space 2} -.160685{col 40}{space 2} .0480794{col 51}{space 1}   -3.34{col 60}{space 3}0.001{col 68}{space 4}-.2549189{col 81}{space 3} -.066451
{txt}{space 2}startcohort_2014_itstart {c |}{col 28}{res}{space 2}-.0515909{col 40}{space 2} .0546924{col 51}{space 1}   -0.94{col 60}{space 3}0.346{col 68}{space 4}-.1587861{col 81}{space 3} .0556043
{txt}{space 2}startcohort_2015_itstart {c |}{col 28}{res}{space 2}-.1360049{col 40}{space 2} .0511503{col 51}{space 1}   -2.66{col 60}{space 3}0.008{col 68}{space 4}-.2362577{col 81}{space 3}-.0357522
{txt}{space 2}startcohort_2016_itstart {c |}{col 28}{res}{space 2}-.0570333{col 40}{space 2} .0768789{col 51}{space 1}   -0.74{col 60}{space 3}0.458{col 68}{space 4}-.2077131{col 81}{space 3} .0936466
{txt}{space 2}startcohort_2017_itstart {c |}{col 28}{res}{space 2}-.0167152{col 40}{space 2}  .055526{col 51}{space 1}   -0.30{col 60}{space 3}0.763{col 68}{space 4}-.1255441{col 81}{space 3} .0921137
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m6f if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m6f if e(sample), residuals
{res}{txt}(5,727 missing values generated)

{com}. 
. gen sse_m6f = predsy_m6f * predsy_m6f if e(sample)
{txt}(5,727 missing values generated)

{com}. gen ssr_m6f = residsy_m6f * residsy_m6f if e(sample)
{txt}(5,727 missing values generated)

{com}. 
. egen sum_sse_m6f = total(sse_m6f) if e(sample)
{txt}(5,726 missing values generated)

{com}. egen sum_ssr_m6f = total(ssr_m6f) if e(sample)
{txt}(5,726 missing values generated)

{com}. 
. gen r2_m6f = sum_ssr_m6f/(sum_sse_m6f + sum_ssr_m6f)
{txt}(5,726 missing values generated)

{com}. 
. sum r2_m6f

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}r2_m6f {c |}{res}      6,087    .3591262           0   .3591262   .3591262
{txt}
{com}. 
. 
. *
. *
. *
. *
. * [MODEL F6: RELATIVE TYPE I PROGRAM ERROR RATE] FIGURE F4D: UNCONDITIONAL ADAPTATION EFFECTS --  E(Y) [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:6,086}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .2610949{col 26}{space 2} .0266351{col 37}{space 1}    9.80{col 46}{space 3}0.000{col 54}{space 4} .2088911{col 67}{space 3} .3132987
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .2851226{col 26}{space 2} .0147998{col 37}{space 1}   19.27{col 46}{space 3}0.000{col 54}{space 4} .2561156{col 67}{space 3} .3141297
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  .327801{col 26}{space 2} .0284566{col 37}{space 1}   11.52{col 46}{space 3}0.000{col 54}{space 4} .2720271{col 67}{space 3} .3835749
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .3201452{col 26}{space 2} .0271524{col 37}{space 1}   11.79{col 46}{space 3}0.000{col 54}{space 4} .2669276{col 67}{space 3} .3733629
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .3128296{col 26}{space 2} .0255844{col 37}{space 1}   12.23{col 46}{space 3}0.000{col 54}{space 4} .2626852{col 67}{space 3} .3629741
{txt}{space 10}6  {c |}{col 14}{res}{space 2}  .306965{col 26}{space 2} .0243874{col 37}{space 1}   12.59{col 46}{space 3}0.000{col 54}{space 4} .2591666{col 67}{space 3} .3547633
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .3024354{col 26}{space 2} .0234778{col 37}{space 1}   12.88{col 46}{space 3}0.000{col 54}{space 4} .2564197{col 67}{space 3} .3484511
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2991253{col 26}{space 2} .0227919{col 37}{space 1}   13.12{col 46}{space 3}0.000{col 54}{space 4} .2544541{col 67}{space 3} .3437965
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .2969189{col 26}{space 2} .0222884{col 37}{space 1}   13.32{col 46}{space 3}0.000{col 54}{space 4} .2532345{col 67}{space 3} .3406033
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2957003{col 26}{space 2}  .021949{col 37}{space 1}   13.47{col 46}{space 3}0.000{col 54}{space 4} .2526812{col 67}{space 3} .3387195
{txt}{space 9}11  {c |}{col 14}{res}{space 2}  .295354{col 26}{space 2}  .021775{col 37}{space 1}   13.56{col 46}{space 3}0.000{col 54}{space 4} .2526757{col 67}{space 3} .3380322
{txt}{space 9}12  {c |}{col 14}{res}{space 2}  .295764{col 26}{space 2} .0217828{col 37}{space 1}   13.58{col 46}{space 3}0.000{col 54}{space 4} .2530706{col 67}{space 3} .3384574
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE F4D{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Relative Type I Program Error Rate [MODEL F6]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
> xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount_placebo}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F6.FIGURE F4D.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F6.FIGURE F4D.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F6.FIGURE F4D.04-10-2025.gph} saved

{com}. 
. *
. *
. *
. *
. * [MODEL F6: RELATIVE TYPE I PROGRAM ERROR RATE] FIGURE F4E: MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (relt1_interstate_catF2==2) & LOW COMPLEXITY (relt1_interstate_catF2==0) VALUES [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]: ***
. 
. margins r.relt1_interstate_catF2 if relt1_interstate_catF2==0|relt1_interstate_catF2==2, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,090}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 27}{c TT}{hline 11}{hline 12}{hline 11}
{col 28}{text}{c |}         df{col 40}        chi2{col 52}     P>chi2
{res}{col 1}{text}{hline 27}{c +}{hline 11}{hline 12}{hline 11}
relt1_interstate_catF2@_at {c |}
{space 14}(2 vs 0)  1  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.06{col 52}{space 2}   0.8039
{txt}{space 14}(2 vs 0)  2  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.03{col 52}{space 2}   0.8601
{txt}{space 14}(2 vs 0)  3  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.21{col 52}{space 2}   0.6473
{txt}{space 14}(2 vs 0)  4  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.38{col 52}{space 2}   0.5385
{txt}{space 14}(2 vs 0)  5  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.61{col 52}{space 2}   0.4336
{txt}{space 14}(2 vs 0)  6  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.87{col 52}{space 2}   0.3518
{txt}{space 14}(2 vs 0)  7  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     1.04{col 52}{space 2}   0.3088
{txt}{space 14}(2 vs 0)  8  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     1.03{col 52}{space 2}   0.3090
{txt}{space 14}(2 vs 0)  9  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.88{col 52}{space 2}   0.3478
{txt}{space 14}(2 vs 0) 10  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.66{col 52}{space 2}   0.4174
{txt}{space 14}(2 vs 0) 11  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.43{col 52}{space 2}   0.5119
{txt}{space 14}(2 vs 0) 12  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.23{col 52}{space 2}   0.6279
{col 1}{text}                    Joint {col 28}{c |}{result}  (not testable)
{col 1}{text}{hline 27}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 28}{c |}{col 40} Delta-method
{col 28}{c |}   Contrast{col 40}   std. err.{col 52}     [95% con{col 65}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
relt1_interstate_catF2@_at {c |}
{space 14}(2 vs 0)  1  {c |}{col 28}{res}{space 2}-.0040287{col 40}{space 2} .0162227{col 51}{space 5}-.0358246{col 65}{space 3} .0277671
{txt}{space 14}(2 vs 0)  2  {c |}{col 28}{res}{space 2} .0034025{col 40}{space 2} .0193079{col 51}{space 5}-.0344402{col 65}{space 3} .0412453
{txt}{space 14}(2 vs 0)  3  {c |}{col 28}{res}{space 2} .0192362{col 40}{space 2}  .042048{col 51}{space 5}-.0631763{col 65}{space 3} .1016487
{txt}{space 14}(2 vs 0)  4  {c |}{col 28}{res}{space 2}  .021203{col 40}{space 2} .0344718{col 51}{space 5}-.0463605{col 65}{space 3} .0887665
{txt}{space 14}(2 vs 0)  5  {c |}{col 28}{res}{space 2} .0219469{col 40}{space 2}  .028029{col 51}{space 5}-.0329889{col 65}{space 3} .0768828
{txt}{space 14}(2 vs 0)  6  {c |}{col 28}{res}{space 2}  .021878{col 40}{space 2} .0234975{col 51}{space 5}-.0241763{col 65}{space 3} .0679323
{txt}{space 14}(2 vs 0)  7  {c |}{col 28}{res}{space 2} .0210784{col 40}{space 2} .0207094{col 51}{space 5}-.0195114{col 65}{space 3} .0616681
{txt}{space 14}(2 vs 0)  8  {c |}{col 28}{res}{space 2} .0196302{col 40}{space 2} .0192973{col 51}{space 5}-.0181919{col 65}{space 3} .0574523
{txt}{space 14}(2 vs 0)  9  {c |}{col 28}{res}{space 2} .0176157{col 40}{space 2}  .018762{col 51}{space 5}-.0191572{col 65}{space 3} .0543885
{txt}{space 14}(2 vs 0) 10  {c |}{col 28}{res}{space 2} .0151169{col 40}{space 2} .0186421{col 51}{space 5} -.021421{col 65}{space 3} .0516548
{txt}{space 14}(2 vs 0) 11  {c |}{col 28}{res}{space 2} .0122161{col 40}{space 2} .0186258{col 51}{space 5}-.0242898{col 65}{space 3}  .048722
{txt}{space 14}(2 vs 0) 12  {c |}{col 28}{res}{space 2} .0089955{col 40}{space 2} .0185583{col 51}{space 5}-.0273782{col 65}{space 3} .0453691
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE F4E{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Relative Type I Program Error Rate [MODEL F6]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount_placebo}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F6.FIGURE F4E.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F6.FIGURE F4E.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F6.FIGURE F4E.04-10-2025.gph} saved

{com}. *
. *
. *
. *
. *
. 
. * [MODEL F6: RELATIVE TYPE I PROGRAM ERROR RATE] FIGURE F4F: MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (relt1_diffoccupseek_catF2==2) & LOW COMPLEXITY (relt1_diffoccupseek_catF2==0) VALUES [WITH RESPECT TO MONTHS SINCE PROJECT START (t + s) : 0 1 6 12.....60]: ***
. 
. margins r.relt1_diffoccupseek_catF2 if relt1_diffoccupseek_catF2==0|relt1_diffoccupseek_catF2==2, at(itmod_monthcount_placebo=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,052}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcou~o} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 30}{c TT}{hline 11}{hline 12}{hline 11}
{col 31}{text}{c |}         df{col 43}        chi2{col 55}     P>chi2
{res}{col 1}{text}{hline 30}{c +}{hline 11}{hline 12}{hline 11}
relt1_diffoccupseek_catF2@_at {c |}
{space 17}(2 vs 0)  1  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     1.88{col 55}{space 2}   0.1699
{txt}{space 17}(2 vs 0)  2  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     5.76{col 55}{space 2}   0.0164
{txt}{space 17}(2 vs 0)  3  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     3.79{col 55}{space 2}   0.0516
{txt}{space 17}(2 vs 0)  4  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     3.61{col 55}{space 2}   0.0576
{txt}{space 17}(2 vs 0)  5  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     3.15{col 55}{space 2}   0.0761
{txt}{space 17}(2 vs 0)  6  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     2.35{col 55}{space 2}   0.1257
{txt}{space 17}(2 vs 0)  7  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     1.40{col 55}{space 2}   0.2365
{txt}{space 17}(2 vs 0)  8  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     0.64{col 55}{space 2}   0.4244
{txt}{space 17}(2 vs 0)  9  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     0.20{col 55}{space 2}   0.6543
{txt}{space 17}(2 vs 0) 10  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     0.03{col 55}{space 2}   0.8694
{txt}{space 17}(2 vs 0) 11  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     0.00{col 55}{space 2}   0.9609
{txt}{space 17}(2 vs 0) 12  {res}{col 31}{text}{c |}{result}{space 2}        1{col 43}{space 3}     0.04{col 55}{space 2}   0.8404
{col 1}{text}                       Joint {col 31}{c |}{result}  (not testable)
{col 1}{text}{hline 30}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 31}{c |}{col 43} Delta-method
{col 31}{c |}   Contrast{col 43}   std. err.{col 55}     [95% con{col 68}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
relt1_diffoccupseek_catF2@_at {c |}
{space 17}(2 vs 0)  1  {c |}{col 31}{res}{space 2} .0214455{col 43}{space 2} .0156256{col 54}{space 5}-.0091801{col 68}{space 3}  .052071
{txt}{space 17}(2 vs 0)  2  {c |}{col 31}{res}{space 2} .0436712{col 43}{space 2} .0182006{col 54}{space 5} .0079987{col 68}{space 3} .0793438
{txt}{space 17}(2 vs 0)  3  {c |}{col 31}{res}{space 2} .0786398{col 43}{space 2} .0403995{col 54}{space 5}-.0005417{col 68}{space 3} .1578213
{txt}{space 17}(2 vs 0)  4  {c |}{col 31}{res}{space 2} .0630962{col 43}{space 2} .0332303{col 54}{space 5}-.0020341{col 68}{space 3} .1282264
{txt}{space 17}(2 vs 0)  5  {c |}{col 31}{res}{space 2} .0485044{col 43}{space 2} .0273429{col 54}{space 5}-.0050867{col 68}{space 3} .1020956
{txt}{space 17}(2 vs 0)  6  {c |}{col 31}{res}{space 2} .0359431{col 43}{space 2} .0234716{col 54}{space 5}-.0100603{col 68}{space 3} .0819465
{txt}{space 17}(2 vs 0)  7  {c |}{col 31}{res}{space 2} .0252808{col 43}{space 2} .0213568{col 54}{space 5}-.0165779{col 68}{space 3} .0671394
{txt}{space 17}(2 vs 0)  8  {c |}{col 31}{res}{space 2} .0163858{col 43}{space 2} .0205132{col 54}{space 5}-.0238193{col 68}{space 3} .0565908
{txt}{space 17}(2 vs 0)  9  {c |}{col 31}{res}{space 2} .0091266{col 43}{space 2} .0203799{col 54}{space 5}-.0308172{col 68}{space 3} .0490705
{txt}{space 17}(2 vs 0) 10  {c |}{col 31}{res}{space 2} .0033718{col 43}{space 2} .0205077{col 54}{space 5}-.0368225{col 68}{space 3} .0435661
{txt}{space 17}(2 vs 0) 11  {c |}{col 31}{res}{space 2}-.0010103{col 43}{space 2} .0206258{col 54}{space 5} -.041436{col 68}{space 3} .0394155
{txt}{space 17}(2 vs 0) 12  {c |}{col 31}{res}{space 2}-.0041511{col 43}{space 2} .0206166{col 54}{space 5} -.044559{col 68}{space 3} .0362567
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE F4F{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Relative Type I Program Error Rate [MODEL F6]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since Project Start", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount_placebo}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F6.FIGURE F4F.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F6.FIGURE F4F.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model F6.FIGURE F4F.04-10-2025.gph} saved

{com}. 
. 
. 
. *********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\OUTPUT\Performance Management.APPENDIX F MODELS.F4_F6.04-10-2025.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}11 Apr 2025, 05:32:56
{txt}{.-}
{smcl}
{txt}{sf}{ul off}